analytics engineer

Shake Shack

<div><div><div><div>225 Varick Street, Suite 301, New York NY 10014, United States
 </div><div>9000 SS - NY - Home Office
</div></div><div><div><div>Our secret to leading the way in hospitality? We put our people first!
At Shake Shack, our mission is to Stand For Something Good in all that we do. From our teams to our neighborhoods, we're committed to always doing the right thing. As one of the fastest-growing hospitality brands, we're all about crafting unforgettable experiences for our guests. We offer endless learning opportunities and the chance to make a lasting impact on our business, restaurants, and communities. As a member of the #ShackFam, you&rsquo;ll have access to hands-on mentorship, training, and growth potential, all in a fun and inclusive environment.
Join us and Be a Part of Something Good.
Job Summary 
We are seeking an Analytics Engineer with a quality-first mindset to join our Data &amp; Analytics team. This role is responsible for designing, building, and maintaining robust data models, pipelines, and analytics infrastructure across a broad, multi-domain portfolio, while simultaneously serving as the internal technical quality gate for a delivery model that includes our internal team and external service partners. The Analytics Engineer: Data Quality Lead bridges hands-on engineering with oversight and standards-setting, ensuring that what gets built is not only functional but reliable, documented, and trustworthy. This role operates within a modern data stack environment and is expected to leverage AI tooling as a core part of day-to-day workflow, accelerating both personal output and broader team capability.
The ideal candidate has 3+ years of analytics engineering experience with strong dbt and SQL proficiency, a track record of working in vendor or offshore delivery models, and the technical judgment to review others' code with precision and confidence. They understand that data quality is not a phase at the end of a project but a discipline embedded in every model, every test, and every deployment decision. They get energy from making systems more reliable, not just shipping their own work. They are genuinely AI-native, using tools like GitHub Copilot, Cursor, or Claude not occasionally, but as a primary accelerant. And they have the communication skills to hold service partners to a high standard while remaining a collaborative, trusted partner to the broader team.
Responsibilities:
Hands-On Build &amp; Engineering<ul><li>
Design, develop, and maintain dbt models, SQL transformations, and data pipelines that produce clean, analytics-ready datasets supporting reporting, analysis, ML/AI, and strategic initiatives across multiple business domains</li><li>
Build and optimize dimensional data models that enable self-service analytics and support advanced use cases including machine learning feature engineering and AI model training</li><li>
Own high-complexity internal workstreams such as semantic layer definitions, cross-domain data models, and metrics standardization where internal technical ownership is critical</li><li>
Support query performance optimization and data warehouse efficiency to reduce cost and improve end-user experience</li><li>
Develop and maintain clear documentation of data models, business logic, and data lineage to promote transparency and enable knowledge sharing across the team</li></ul>
Technical Quality &amp; Service Partner Oversight<ul><li>
Serve as the internal technical quality gate for service partner deliverables, reviewing pull requests and outputs against established data modeling standards, testing requirements, and documentation expectations</li><li>
Use AI-assisted code review tooling to conduct scalable first-pass analysis of service partner code, focusing human judgment on highest-risk decisions and architectural patterns</li><li>
Own and continuously improve the team's data observability posture by deploying and tuning monitoring tools (e.g., Elementary, re_data, Monte Carlo, or Soda) to detect anomalies, freshness failures, and quality regressions before they surface in dashboards or downstream systems</li><li>
Build and enforce pre-deployment checklists and release gate criteria, including automated downstream impact assessments so no change ships without a known blast radius</li><li>
Define and maintain data contracts between data producers and consumers, creating explicit, documented agreements about what each dataset guarantees to reduce silent failures and undocumented assumptions</li><li>
Provide technical guidance and mentorship to service partner resources and extended team members, raising overall delivery quality across the ecosystem</li></ul>
Cross-Functional Collaboration &amp; Stakeholder Partnership<ul><li>
Partner with the Business Analyst, Data Product Lead, and Product Manager to translate business requirements into scalable, well-scoped data solutions</li><li>
Collaborate with Data Engineering to ensure reliable upstream pipelines and with analytics consumers across Operations, Finance, Marketing, and other business units to understand data needs and validate that delivered solutions drive intended outcomes</li><li>
Act as a trusted technical voice in program and project delivery conversations, flagging quality or capacity risks before they become deployment failures</li></ul>
Minimum Qualification:<ul><li>
3+ years of experience in analytics engineering, data modeling, or a closely related data delivery role focused on transforming raw data into analytics-ready datasets</li><li>
Strong proficiency in SQL and hands-on experience with dbt, including testing frameworks, documentation standards, and model governance</li><li>
Experience working with cloud data warehouses (Snowflake, BigQuery, Redshift, or similar)</li><li>
Demonstrated understanding of data modeling concepts including dimensional modeling, star/snowflake schemas, and normalization</li><li>
Experience working in a delivery model that includes offshore, vendor, or service partner resources, with direct accountability for reviewing and accepting their technical output</li><li>
Familiarity with data quality monitoring concepts including row count validation, freshness checks, null rate monitoring, and referential integrity testing</li><li>
Active, daily use of AI coding assistants (GitHub Copilot, Cursor, Claude, or similar) as a core part of engineering workflow rather than occasional experimentation</li><li>
Familiarity with version control (Git) and software engineering best practices including branching, code review, and CI/CD concepts</li><li>
Strong analytical thinking and attention to detail, with a demonstrated ability to identify and resolve data quality issues before they reach end users</li><li>
Effective communication and stakeholder management skills, with the ability to give direct technical feedback to partners and translate data concepts for non-technical audiences</li><li>
Bachelor's Degree in Computer Science, Information Systems, Data Analytics, Statistics, or a related field, or equivalent practical experience</li></ul>
Preferred Qualifications:<ul><li>
Hands-on experience with data observability platforms such as Elementary, re_data, Monte Carlo, or Soda Core</li><li>
Experience defining or enforcing data contracts or SLA-style quality agreements between data producers and consumers</li><li>
Experience with CI/CD pipeline concepts applied to dbt projects, including automated testing gates and deployment workflows</li><li>
Knowledge of Python for data transformation, automation, or pipeline scripting</li><li>
Experience with data orchestration tools such as Airflow, Dagster, or Prefect</li><li>
Familiarity with business intelligence and data visualization tools (Tableau, Looker, Power BI, or similar), with an understanding of how upstream model decisions affect downstream reporting</li><li>
Understanding of machine learning workflows and feature engineering requirements, including how to structure data models for ML model training and validation</li><li>
Experience in retail, hospitality, QSR, or restaurant operations environments</li><li>
Exposure to event tracking, product analytics, or marketing analytics data domains</li><li>
Experience collaborating with Data Science teams on analytical or ML projects, translating modeling requirements into data product specifications</li><li>
5+ years of total experience in analytics engineering, data analysis, or a related field</li></ul>
Benefits at Shake Shack:
A work environment where you can come as you are, share your ideas, have fun, and work collaboratively:<ul><li>
Weekly Pay and Performance bonuses</li><li>
Shake Shack Meal Discounts</li><li>
Exclusive corporate discounts for travel, electronics, wellness, leisure activities and more</li><li>
Medical, Dental, and Vision Insurance*</li><li>
Employer Paid Life and Disability Insurance*</li><li>
401k Plan with Company Match*</li><li>
Paid Time Off*</li><li>
Paid Parental Leave*</li><li>
Access to Employee Assistance Program on Day 1</li><li>
Pre-Tax Commuter and Parking Benefits</li><li>
Flexible Spending and Dependent Care Accounts*</li><li>
Development and Growth Opportunities</li></ul><ul><li>Eligibility criteria applies</li></ul>
Pay Range - $121,650.00 - $159,650.00 
About Us
Beginning as a hot dog cart in New York City&rsquo;s Madison Square Park, Shake Shack was created by Danny Meyer, Founder and CEO of Union Square Hospitality Group and best-selling author of Setting the Table. Shack Fans lined up daily, making the cart a resounding success, and donating all proceeds back to the park beautification efforts. A permanent stand was eventually built&hellip;and the rest is Shack history! With our roots in fine dining and giving back to the community, we are committed to high quality food served with a high level of hospitality. Our team members enjoy a positive work environment that is deeply committed to the philosophy that we &quot;Stand for Something Good.&quot;
Shake Shack is an Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to any protected characteristic, including race, color, ancestry, national origin, religion, creed, age, disability (mental and physical), sex, gender identity, sexual orientation, gender expression, medical condition, genetic information, marital, military and veteran status.</div> 
</div></div>
</div><h3 class="jobSectionHeader">Job Details</h3>
<div>
<div>Job Category:
Support Center
</div><div>Position Type:
Full Time
</div><div>Job ID:
JR12307</div></div></div>

Analytics Engineer – Data Quality Lead

Stott and May

<div>
 
 
 Data Scientist / Data Engineer (Ecommerce) Contract Opportunity
 
 
 
 
 
 
 
 Work:
 
 
 
 We&rsquo;re supporting an ecommerce client looking for a hybrid
 
 Data Scientist / Data Engineer
 
 contractor to help scale data products and drive insights across customer, product, and digital performance data.
 
 
 
 
 
 
 Responsibilities:
 
 
 <ul>
 <li>
 Build and maintain data pipelines supporting analytics and machine learning use cases
 </li>
 <li>
 Develop models and analyses around customer behavior, recommendations, forecasting, and conversion optimization
 </li>
 <li>
 Work with large-scale ecommerce data (clickstream, transactions, inventory, marketing)
 </li>
 <li>
 Design and optimize datasets for reporting, experimentation, and predictive modeling
 </li>
 <li>
 Partner with analytics, product, and engineering teams to productionize solutions
 </li>
 <li>
 Support data quality, performance tuning, and scalable data architecture
 </li>
 </ul>
 
 
 Requirements:
 
 
 <ul>
 <li>
 Strong Python and SQL skills
 </li>
 <li>
 Experience in both data engineering and data science / ML workflows
 </li>
 <li>
 Background building pipelines with tools like Airflow, Spark, dbt, or similar
 </li>
 <li>
 Experience with cloud platforms such as AWS, GCP, or Azure
 </li>
 <li>
 Exposure to recommendation systems, forecasting, customer analytics, or experimentation (A/B testing) preferred
 </li>
 <li>
 Ecommerce, retail, or marketplace data experience strongly preferred
 </li>
 </ul>
 
 
 Details:
 
 
 <ul>
 <li>
 Fully remote
 </li>
 <li>
 40 hours/week (full-time
 
 contract
 
 )
 </li>
 <li>
 Initial 6-month
 
 contract
 
 with likely extension
 </li>
 <li>
 W2 or personal LLC only (
 
 no C2C
 
 )
 </li>
 <li>
 Rates competitive and dependent on experience (target $80&ndash;$110/hr)
 </li>
 </ul>
 
 
</div>

Data Scientist

James Search Group

<div>
 
 
 AI/ML Engineer
 
 
 
 
 Location:
 
 New York, NY
 
 
 
 Compensation:
 
 $135,000 &ndash; $175,000 base + bonus + full benefits, (401(k), and more)
 
 
 
 Industry:
 
 Global P&amp;C Insurance
 
 
 
 
 
 
 About the role and the company.
 
 
 
 James Search Group is working closely with a top-tier global property &amp; casualty insurance carrier to identify an
 
 AI/ML Engineer
 
 to join their enterprise Data and Artificial Intelligence division. This team is driving innovation across the business by developing and scaling AI capabilities that unlock new value and improve decision-making across underwriting, claims, and customer engagement.
 
 
 
 
 
 As an AI/ML Engineer, you'll collaborate with senior team members to build, refine, and deploy predictive models and intelligent systems. The ideal candidate is skilled in analytics, curious by nature, and passionate about transforming complex data into practical solutions that deliver measurable business impact.
 
 
 
 
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Support the development and deployment of AI/ML models&mdash;including classification, forecasting, uplift and propensity modeling, and foundational model tuning.
 </li>
 <li>
 Collaborate with cross-functional teams to identify use cases where data science can enhance operational efficiency and business performance.
 </li>
 <li>
 Partner with business, IT, and strategy teams to ensure AI models are scalable, impactful, and aligned with organizational goals.
 </li>
 <li>
 Communicate analytical findings clearly to stakeholders with varying levels of technical knowledge.
 </li>
 </ul>
 
 
 
 
 
 Required Qualifications:
 
 
 <ul>
 <li>
 Master&rsquo;s degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative discipline; Ph.D. a plus.
 </li>
 <li>
 Minimum of 2 years&rsquo; experience applying advanced analytics methods (e.g., decision trees, logistic regression, neural networks, random forests).
 </li>
 <li>
 Proficiency in R or Python for analytics and model development, including work with both structured and unstructured data.
 </li>
 <li>
 Proven ability to manage and integrate large, complex datasets and an understanding of end-to-end data modeling processes.
 </li>
 <li>
 Strong written and verbal communication skills with the ability to connect technical outputs to business strategy.
 </li>
 </ul>
 
 
 
 
 
 Preferred Qualifications:
 
 
 <ul>
 <li>
 Internship or project experience in machine learning, AI engineering, or related roles&mdash;especially in insurance or financial services.
 </li>
 <li>
 Familiarity with actuarial concepts or insurance-specific data models.
 </li>
 <li>
 Enthusiastic about continued learning in AI/ML and staying current with new tools and methodologies.
 </li>
 <li>
 Comfortable in a collaborative, fast-paced, and agile working environment.
 </li>
 </ul>
 
 
 
 
 This is a unique opportunity to grow your career with a highly respected organization investing in next-generation analytics.
 
 
 
 
 
 
 Apply today
 
 to help reshape the future of insurance through data and AI innovation.
 
</div>

Machine Learning Engineer

Kaizen

<div>
 
 Who Are We?
 
 
 
 Government technology has failed the public for decades, and Americans have been conditioned to expect websites from the 90s for essential public services.
 
 
 Kaizen exists to strengthen trust in American public services by building technology that residents and public servants are proud to use. We partner with local, state, and federal agencies to replace legacy systems with modern, AI-native software that is worthy of the people they serve. We started in outdoor recreation, and now we're building toward something much larger &mdash; the software layer that powers how Americans access any government service.
 
 
 Our platform already reaches 40 million residents across 50+ agencies in 17 states.
 
 
 Founded in 2022 and based in New York City, Kaizen has raised $35 million from NEA, a16z, Accel, 776, and Carpenter Capital. We're builders, designers, and operators who believe that beautifully designed software shouldn&rsquo;t be a luxury in government. It&rsquo;s how you earn trust back.
 
 
 
 The Data Intelligence Team
 
 
 
 The DI team exists to make Kaizen faster, smarter, and more effective at every level. The team runs four pillars:
 
 
 <ul>
 <li>
 External Analytics: Best-in-class reporting for customers inside Kaizen's platform
 </li>
 <li>
 Automation: Finding repetitive workflows across the org and building tools to eliminate them
 </li>
 <li>
 AI Enablement: Making sure every person at Kaizen knows how to use AI effectively
 </li>
 <li>
 Internal Analytics: Trusted dashboards and data models that give every level of the org what they need to make faster decisions
 
 
 </li>
 </ul>
 
 The Role
 
 
 
 We&rsquo;re looking for a Data Engineer who can operate across the full spectrum of modern data work. This is not a narrow role. You will build and maintain the data models that power our internal and customer-facing reporting, design and ship automations that eliminate repetitive manual work, develop AI-powered tools that make teams faster and smarter, and build the internal tools that keep the whole operation running. One week you might be writing dbt models and tuning a Snowflake pipeline. The next you are building an internal application that transforms how a team operates, or standing up an AI workflow that cuts hours of manual work down to minutes.
 
 
 We are looking for someone who brings both technical depth and business instinct. You will work directly with team leads across the company, translate their problems into solutions, and own those solutions from concept to production. If you are energized by variety, thrive without a lot of hand-holding, and want your work to have a direct and visible impact on a growing company, this is the role.
 
 
 
 Who You Are
 
 
 
 You are a versatile data and analytics professional who thrives in ambiguity. You do not wait to be told what to build - you find the problem, propose the solution, and ship it. You are equally comfortable writing a dbt model, standing up an N8N automation, and pulling an ad hoc analysis for a leadership meeting. You care about doing clean work, and you understand that in a small team, your output is highly visible.
 
 
 
 What You Will Do
 
 
 
 <ul>
 <li>
 Build and maintain dbt models that power internal dashboards and external customer reporting
 </li>
 <li>
 Develop and maintain automation workflows in N8N and similar tools, eliminating repetitive manual work across the org
 </li>
 <li>
 Support the build-out of external analytics inside Kaizen's platform, ensuring customers have clear, actionable data at their fingertips
 </li>
 <li>
 Contribute to internal dashboards and analyses that keep leadership and team leads informed
 </li>
 <li>
 Partner with the team lead to identify and prioritize new automation and analytics opportunities across the organization
 </li>
 <li>
 Help shape the analytics layer of Kaizen's platform as the product scales
 
 
 </li>
 </ul>
 
 What You Bring
 
 
 
 <ul>
 <li>
 3-5 years of experience in a data, analytics, or engineering role (slope matters more than seniority)
 </li>
 <li>
 Strong SQL skills: you can write complex queries and think in data models
 </li>
 <li>
 Hands-on experience with dbt (or a strong willingness to ramp quickly)
 </li>
 <li>
 Experience building workflows and automations in tools like N8N, Zapier, or similar tools
 </li>
 <li>
 Comfort working across a modern data stack (Snowflake or similar)
 </li>
 <li>
 Understanding a second programming language is a strong plus (Python, JavaScript, etc.)
 </li>
 <li>
 Genuine fluency with AI tools: not just using them, but building with them
 </li>
 <li>
 Low ego, high output: you are a builder who gets things done without a lot of hand-holding
 </li>
 <li>
 Experience in a fast-paced startup environment
 </li>
 <li>
 Genuine curiosity about AI and how it can be applied to real workflows
 
 
 </li>
 </ul>
 
 What Kaizen Offers
 
 
 
 <ul>
 <li>
 Comprehensive medical through Oxford/United, including Gold and Platinum PPO plans, with 85% of premiums covered on the Platinum plan and a $0 employee premium option
 </li>
 <li>
 Dental through Guardian PPO and vision through Beam, with 99% of employee premiums covered and 50% for dependents
 </li>
 <li>
 $100,000 in fully paid life insurance. FSA and Dependent Care FSA. 401(k) access through Guideline
 </li>
 <li>
 16 weeks of fully paid parental leave for birthing parents. 10 weeks fully paid for non-birthing parents
 </li>
 <li>
 Unlimited PTO and closed for all federal holidays
 </li>
 <li>
 Company-wide winter break the week of Christmas
 </li>
 <li>
 One-time home office or desk setup stipend up to $750
 </li>
 <li>
 $50/month commuter benefit
 </li>
 <li>
 $100/month for wellness or productivity; your call on how you use it
 </li>
 <li>
 $500/year for professional development
 </li>
 <li>
 $250/year for recreation
 </li>
 <li>
 Company-provided laptop
 </li>
 <li>
 Expensed lunch while in the office
 
 
 </li>
 </ul>
 
 Kaizen is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, age, sex, national origin, disability, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local law. If you need an accommodation during the interview process, email us at
 
 
 careers@kaizenlabs.co
 
 
 .
 
 
 
 Why Now
 
 
 The government technology modernization wave is happening. Agencies are being pushed to change, and they need partners who can move fast and build well.
 
 
 We have the contracts. We have the agency relationships. We have the platform. We need the engineers.
 
 
 If you want to spend the next few years shipping software that actually matters &mdash; not another B2B SaaS dashboard, not an internal tool nobody uses &mdash; and you want to do it at a company moving aggressively into one of the most consequential markets in tech, this is the role.
 
 
 
 Don't Apply If...
 
 
 
 You need a detailed ticket to know what to build next. You think AI tools are a crutch. You want to own one layer of the stack and never leave it. You're more comfortable in a 500-person organization than a small team where everyone's accountable. You need the problem fully defined before you start moving. Or if you're looking for low-stakes work &mdash; because what we build touches real people in real moments that matter: accessing benefits, families navigating public services, constituents interacting with their government at its most critical touchpoints. This work is serious. The people we serve deserve serious teams.
</div>

Data Engineer

Paxos

<div>
 
 About Paxos
 
 
 
 Today&rsquo;s financial infrastructure is archaic, expensive, inefficient and risky &mdash; supporting a system that leaves out more people than it lets in. So we&rsquo;re rebuilding it.
 
 
 We&rsquo;re on a mission to open the world&rsquo;s financial system to everyone by enabling the instant movement of any asset, any time, in a trustworthy way. For over a decade, we&rsquo;ve built blockchain infrastructure that tokenizes, custodies, trades and settles assets for the world&rsquo;s leading financial institutions, like Mastercard, Visa, Robinhood, and PayPal.
 
 
 
 About The Team
 
 
 
 The Analytics team is at the center of data-driven decision making at Paxos. We are a dedicated group of analysts and data professionals who transform raw data into insights that guide our product, operations, risk, and commercial strategies. Our team is passionate about building trustworthy metrics, intuitive dashboards, and clear analyses that help stakeholders move faster and with more confidence. We partner closely with teams across the company, leveraging deep understanding of our business and products to solve complex problems and unlock new opportunities through data.
 
 
 
 About The Role
 
 
 
 As a Data Analyst at Paxos, you will own analytics for Paxos stablecoins and rewards initiatives. You will combine strong SQL, dashboarding, and analytical skills with a product mindset to help teams make better decisions, design smarter incentives, and meet our regulatory and reporting obligations. You&rsquo;ll work closely with Product, Engineering, Finance, and Account Management to turn complex data into clear, actionable insights in a lean, high‑ownership environment
 
 
 
 What You&rsquo;ll Do
 
 
 
 <ul>
 <li>
 Own the analytics for Paxos-issued tokens and Rewards as a product, including defining core metrics, building dashboards, and creating analyses that inform product, rewards, and network strategy.
 </li>
 <li>
 Translate ambiguous business questions from Product, Engineering, Finance, and Account Management into well‑scoped data problems and deliver clear, decision‑ready outputs (dashboards, analyses, memos, reports).
 </li>
 <li>
 Partner with the data engineering team and cross-functional stakeholders to shape and improve the underlying data models in Snowflake and dbt, ensuring the stablecoin and rewards domains are well‑modeled, trusted, and documented.
 </li>
 <li>
 Use AI‑powered tooling (e.g., Snowflake native AI, Cortex, Claude/Claude Code, Streamlit apps) to accelerate analysis, improve model quality, and prototype new analytics workflows.
 </li>
 <li>
 Contribute to regulatory reporting, audits, and exam requests as part of the team&rsquo;s BAU responsibilities, ensuring accuracy, consistency, and timely delivery.
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 2-5 years of experience in data and analytics roles (e.g., Data Analyst, Analytics Engineer, Technical Product Analyst), ideally in a product or financial/fintech environment.
 </li>
 <li>
 Advanced SQL proficiency, including querying large, complex datasets and reasoning about data model design - not just editing or copy‑pasting existing queries.
 </li>
 <li>
 Hands-on dashboarding and data visualization experience with tools such as Looker, Metabase, or Tableau, including designing new dashboards and metric frameworks rather than only maintaining existing ones.
 </li>
 <li>
 Strong analytical and quantitative skills, with practical experience in metrics development, data modeling concepts, and basic statistics, working on top of a modern warehouse + modeling stack (e.g., Snowflake + dbt or equivalent).
 </li>
 <li>
 Comfort with Git-based workflows for managing analytics code and assets (SQL, dbt models, dashboard definitions), including branching, pull requests, code review, and CI/CD in the data stack.
 </li>
 <li>
 Demonstrated ability to leverage AI in analytics workflows, ideally including Snowflake-native AI (e.g., Cortex), Claude Code, and Streamlit apps to &ldquo;vibe code&rdquo; dashboards or accelerate analysis and modeling work.
 </li>
 <li>
 Excellent written and verbal communication skills, with the ability to explain complex technical and quantitative concepts to non-technical stakeholders and tailor messaging to product, engineering, finance, and account management audiences.
 </li>
 <li>
 Strong stakeholder and requirements-gathering skills: able to collect, challenge, and distill business needs into clear, actionable deliverables (dashboards, analyses, reports), including regulatory reporting, audit, and exam requests as part of BAU work.
 </li>
 <li>
 A growth mindset and high adaptability: eager to learn, comfortable with ambiguity, and willing to experiment with new tools and approaches (including AI), rather than being rigid or &ldquo;set in your ways.&rdquo;
 
 
 </li>
 </ul>
 
 Important Notice for Paxos Applicants
 
 
 
 We&rsquo;ve become aware of fraudulent accounts posting as Paxos recruiters on LinkedIn and other platforms. These scammers attempt to deceive applicants into paying for job opportunities or providing personal financial information.
 
 
 
 To Verify a Legitimate Paxos Recruiter
 
 
 
 <ul>
 <li>
 We only use @paxos.com email addresses
 </li>
 <li>
 We never ask for payment or financial details to apply, interview, or work here
 </li>
 <li>
 For technical roles, we do not perform a coding interview without prior screening by our engineering team
 
 
 </li>
 </ul>
 Thanks for your interest in Paxos!
 
 
 Compensation Range: $146K - $167.7K
 
 
</div>

Data Analyst

Whatnot

<div>
 
 
 🚀 Join the Future of Commerce with Whatnot!
 
 
 
 
 Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
 
 
 As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
 
 
 We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.
 
 
 
 💻 Role
 
 
 
 Data is crucial to Whatnot&rsquo;s mission to bring people together through commerce.
 
 
 As our newest
 
 Data Engineer
 
 , you&rsquo;ll build and scale the systems that power data-driven decisions across the company. You&rsquo;ll work directly with stakeholders across the business &ndash; such as product, sales, marketing, finance, and trust teams &ndash; to design reliable data architectures, ship resilient pipelines, and create the foundational data products that power Whatnot&rsquo;s internal and external growth.
 
 
 
 On Any Given Day, You Will
 
 
 
 <ul>
 <li>
 Own data architecture end-to-end. Define how we capture, model, and serve critical business data&mdash;then implement it in production. You&rsquo;ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency.
 </li>
 <li>
 Build mission-critical pipelines. Develop and operate streaming and batch data workflows that process high-volume events across multiple domains&mdash;user activity, transactions, experimentation, marketing performance, and operational telemetry&mdash;with tight guarantees for latency, completeness, and accuracy.
 </li>
 <li>
 Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and real-time applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams.
 </li>
 <li>
 Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
 </li>
 <li>
 Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
 </li>
 <li>
 Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.
 
 
 </li>
 </ul>
 Due to the cross-functional nature of this role, candidates will be matched to multiple teams based on need and experience, including product, analytics, or data platforms teams.
 
 
 
 👋 You
 
 
 
 People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building. They care more about outcomes than credit and stay close to the product and the people using it.
 
 
 <ul>
 <li>
 Have 3+ years of experience as a data or software engineer building data warehouses, distributed data systems, or event-driven architectures.
 </li>
 <li>
 Can design and implement data models using dimensional, Data Vault, or ledger-style techniques that support analytical and transactional workloads.
 </li>
 <li>
 Have deep hands-on expertise with modern data tooling across ingestion (e.g., Kafka, Debezium), transformation (dbt, Spark, Flink), orchestration (Dagster, Airflow), and observability (Monte Carlo, Great Expectations).
 </li>
 <li>
 Have operated cloud data warehouses such as Snowflake, BigQuery, or Redshift, including schema design, cost optimization, and workload tuning.
 </li>
 <li>
 Are comfortable writing production-grade code in Python or SQL languages, and integrating with CI/CD and infrastructure-as-code workflows.
 </li>
 <li>
 Enjoy partnering across disciplines&mdash;engineering, product, analytics&mdash;to translate messy business requirements into elegant data systems.
 </li>
 <li>
 Thrive as a self-starter in a fast-moving environment, owning both the technical design and the operational outcomes of your work.
 
 
 </li>
 </ul>
 If designing the data backbone that powers Whatnot&rsquo;s growth, insights, and product innovation excites you, we&rsquo;d love to meet you.
 
 
 
 We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.
 
 
 
 
 💰Compensation
 
 
 
 
 For Full-Time (Salary)
 
 
 
 
 US based applicants:
 
 $180,000/year to $260,000/year + benefits + equity.
 
 
 The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity.
 
 
 
 🎁 Benefits
 
 
 
 <ul>
 <li>
 Generous Holiday and Time off Policy
 </li>
 <li>
 Health Insurance options including Medical, Dental, Vision
 </li>
 <li>
 Work From Home Support
 </li>
 <ul>
 <li>
 Home office setup allowance
 </li>
 <li>
 Monthly allowance for cell phone and internet
 </li>
 </ul>
 <li>
 Care benefits
 </li>
 <ul>
 <li>
 Monthly allowance for wellness
 </li>
 <li>
 Annual allowance towards Childcare
 </li>
 <li>
 Lifetime benefit for family planning, such as adoption or fertility expenses
 </li>
 </ul>
 <li>
 Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
 </li>
 <li>
 Monthly allowance to dogfood the app
 </li>
 <ul>
 <li>
 All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).
 </li>
 </ul>
 <li>
 Parental Leave
 </li>
 <ul>
 <li>
 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
 </li>
 </ul>
 </ul>
 
 
 💛 EOE
 
 
 
 
 Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.
 
 
 Compensation Range: $180K - $260K
 
 
</div>

Amazon Web Services

<div><h2 class="jobSectionHeader">DESCRIPTION</h2>
Amazon Web Services (AWS) is a dynamic and rapidly growing business within Amazon and the leader in providing secure, reliable, scalable, and innovative services that help over a million businesses, governments, education, and not for profits across the globe scale and grow! AWS provides a wide set of scalable services to meet customer needs. 
 
At AWS, the Global Deal Strategy and Programs (GDSP) team drives cloud adoption and business growth through innovative pricing strategies. The organization comprises two specialized teams: Strategic Customer Engagements, which guide ** transformative deals with industry leaders, and Private Pricing Programs &amp; Experiences, which scales and optimizes pricing solutions across our diverse customer base. Within GDSP, you will develop deep expertise in cloud economics, hone your strategic thinking, and directly impact AWS's market leadership while working with latest technologies and global clients. 
 
The AWS Global Deal Strategy and Programs (GDSP) organization is responsible for the Private Pricing Program. The Private Pricing Analytics and Insights (PPA&amp;I) team owns building scalable analytical solutions that enable the GDSP organization with actionable insights to make data-driven decisions. This role will focus on Data Engineering, and Analytics related to the Private Pricing Program, requiring deep technical skills, strong business acumen and a deep analytical background to provide actionable data-driven insights and decision support. 
 
As a Data Engineer on this team, you will drive efficiency in data handling processes, setup product analytics, drive advanced analysis and build new metrics which are key inputs to improve the health, scale and growth of the Private Pricing Program. You will work collaboratively with the business leaders operations, and engineering teams on many non-standard and unique business problems and support business initiatives by collecting required and related data from external/internal sources. 
 
The ideal candidate should have strong communication skills and ability to prioritize effectively to ensure timelines are met. You will own projects from engaging with customers to production and delivery of a suite of tools used by the organization to make key business decisions. You should be a self-starter, comfortable with ambiguity, able to think big and be creative (while still paying careful attention to detail). You think in terms of architecture, not just code. You proactively work to improve the consistency and integration between your team&rsquo;s BI solutions and any related systems or artifacts. 
 
Key job responsibilities 
Design, develop, and maintain scalable data solutions for metrics, reports, analyses, and dashboards that support analytical and business needs 
 
Collaborate with technology teams to extract, transform, and load data from diverse sources using AWS services and internal tools 
Build and deliver high-quality datasets that support data science initiatives and customer reporting requirements 
 
Automate and optimize reporting processes to enable self-service analytics for stakeholders 
Translate business problem statements into technical analysis requirements 
 
Apply analytical and statistical rigor to answer business questions and drive data-driven decisions 
Develop measurement frameworks that quantify customer experience and drive business outcomes 
 
Create queries and visualizations for ad-hoc requests, projects, and ongoing reporting needs 
Write efficient, optimized queries with deep knowledge of data structures and query optimization techniques 
 
Adopt best practices in reporting and analysis including data integrity, design, validation, and documentation 
 
Troubleshoot operational data quality issues and audit existing data pipelines and queries 
 
Recommend improvements to back-end data sources for increased accuracy and simplicity<h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>3+ years of data engineering experience</li><li>1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience</li><li>1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience</li><li>1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience</li><li>1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience</li><li>1+ years of developing and operating large-scale data structures for business intelligence analytics using Oracle experience</li><li>Experience with data modeling, warehousing and building ETL pipelines</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions</li><li>Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, NY, New York - 145,300.00 - 196,600.00 USD annually 
USA, TX, Austin - 132,100.00 - 178,800.00 USD annually 
USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually</div>

Data Engineer, AWS GDSP A&I

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