data scientist

data engineer

software engineer

data analyst

data engineer ii

analytics engineer data quality lead

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

NavitasPartners

<div><h3 class="jobSectionHeader">Job Title: Senior Data Engineer</h3>

Location: New York, NY 10020

<h3 class="jobSectionHeader">Position Summary</h3>

We are seeking a highly experienced Senior Data Engineer to support the design, development, and optimization of large-scale data platforms within a financial services environment. This role focuses on building robust data pipelines, enabling advanced credit risk analytics, and ensuring compliance with regulatory standards.

The ideal candidate will bring deep expertise in credit and counterparty risk, modern data engineering technologies, and cloud-based data platforms, while collaborating closely with cross-functional teams including risk, quant, and compliance stakeholders.

<h3 class="jobSectionHeader">Key Responsibilities</h3>
<h4 class="jobSectionHeader">Data Architecture &amp; Engineering</h4>
<ul><li>Lead architecture and technical design discussions for credit risk data platforms</li>
<li>Design and implement scalable batch and streaming data pipelines using PySpark</li>
<li>Build ingestion frameworks for structured and unstructured data from upstream systems into cloud storage (e.g., S3/ADLS)</li>
<li>Implement data processing workflows using Medallion Architecture (Bronze, Silver, Gold layers)</li>
</ul><h4 class="jobSectionHeader">Data Processing &amp; Optimization</h4>
<ul><li>Develop and optimize transformation logic for large-scale financial datasets</li>
<li>Ensure high data quality, auditability, and regulatory compliance</li>
<li>Apply optimization techniques such as partitioning, indexing, and schema evolution</li>
<li>Model and optimize risk metrics (e.g., PD, LGD, EAD, EPE, PFE, CVA) for analytics and reporting</li>
</ul><h4 class="jobSectionHeader">Platform Integration &amp; Operations</h4>
<ul><li>Integrate with external risk engines and support orchestration of complex batch processes</li>
<li>Ensure platform reliability, observability, and data lineage tracking</li>
<li>Implement and maintain security standards (IAM, encryption, authentication protocols)</li>
<li>Troubleshoot production issues and provide ongoing support</li>
</ul><h4 class="jobSectionHeader">Collaboration &amp; Documentation</h4>
<ul><li>Collaborate with data scientists, risk analysts, and business stakeholders</li>
<li>Contribute to API design and data contracts for internal and external consumers</li>
<li>Maintain comprehensive technical documentation for audit and compliance purposes</li>
<li>Participate in Agile development processes and ceremonies</li>
</ul><h3 class="jobSectionHeader">Required Qualifications</h3>
<ul><li>12+ years of experience in data engineering or data development</li>
<li>Strong experience within financial services, particularly credit or counterparty risk</li>
<li>Expertise in regulatory frameworks such as Basel III/IV, IFRS 9, CECL, or FRTB</li>
<li>Advanced proficiency in Python and PySpark / Apache Spark</li>
<li>Hands-on experience with cloud platforms and data lake technologies (e.g., Databricks, Delta Lake)</li>
<li>Strong SQL skills including complex queries, joins, and performance optimization</li>
<li>Experience building data pipelines from multiple financial data sources</li>
<li>Familiarity with workflow orchestration tools (e.g., Airflow or similar)</li>
<li>Experience with CI/CD tools such as Git, Jenkins, or Azure DevOps</li>
<li>Cloud certification (e.g., AWS Certified Cloud Practitioner or equivalent)</li>
</ul><h3 class="jobSectionHeader">Preferred Qualifications</h3>
<ul><li>Experience with large-scale financial risk systems and analytics platforms</li>
<li>Background in designing data architectures and maintaining data dictionaries</li>
<li>Experience working in Agile environments using tools such as JIRA or Confluence</li>
<li>Exposure to modern data governance and compliance practices</li>
</ul><h3 class="jobSectionHeader">Skills &amp; Competencies</h3>
<ul><li>Strong analytical and problem-solving abilities</li>
<li>Excellent communication skills with both technical and non-technical stakeholders</li>
<li>Ability to work collaboratively with cross-functional teams</li>
<li>High attention to detail and commitment to data quality</li>
<li>Proactive, self-driven mindset with continuous learning approach</li>
</ul><h3 class="jobSectionHeader">Work Environment</h3>
<ul><li>Fast-paced, data-driven financial services environment</li>
<li>High level of collaboration with global teams</li>
<li>Focus on regulatory compliance, data governance, and innovation</li>
</ul>
For more details reach at resumes@navitassols.com.</div>

Senior Data Engineer - SDE 26-04140

Louis Vuitton

<div>
 
 About The Job
 
 
 
 Louis Vuitton Americas is seeking a Senior Data Scientist &ndash; Analytics and AI Data to join our Digital team based in New York City. This individual will act as a hybrid data scientist, MLOps engineer, BI SME, business analyst, and data engineer, while owning the roadmap of analytics and AI solutions that can be industrialized and scaled by central teams. You will sit at the intersection of local business needs and global data/AI strategy, ensuring solutions are impactful, scalable, and aligned with enterprise priorities.
 
 
 Job responsibilities
 
 
 <ul>
 <li>
 Product Strategy &amp; Cross-Functional Roadmap:
 
 </li>
 <ul>
 <li>
 Lead the comprehensive analytics and AI roadmap, guiding it from opportunity identification and prioritization through delivery and transition to central teams.
 </li>
 <li>
 Collaborate with diverse functional stakeholders (e-commerce, retail, CRM, supply chain, finance, etc.) to identify use cases, define problem statements, and translate these into clear data product requirements.
 </li>
 <li>
 Establish success metrics, measure value realization, and continuously refine the roadmap based on impact and feedback.
 
 </li>
 </ul>
 <li>
 Data Science &amp; Advanced Analytics:
 
 </li>
 <ul>
 <li>
 Design and lead predictive models and analytical solutions utilizing Python and SQL.
 </li>
 <li>
 Conduct thorough exploratory data analysis, feature engineering, model training, validation, and comprehensive documentation.
 </li>
 <li>
 Develop reusable analytical assets (scores, segments, KPIs) for broad application across various markets and functions.
 
 </li>
 </ul>
 <li>
 MLOps &amp; Data Engineering:
 
 </li>
 <ul>
 <li>
 Collaborate with MLOps and Data Engineering teams to develop, orchestrate, and maintain robust data and machine learning pipelines, leveraging tools such as Dataiku and Google BigQuery.
 </li>
 <li>
 Implement best practices for testing, version control, monitoring, and observability to ensure the creation of reliable, production-grade solutions.
 </li>
 <li>
 Partner with central data engineering and platform teams to align initiatives with enterprise standards and promote scalability.
 </li>
 <li>
 Work alongside business, local, and central Data Engineering teams to facilitate data integration and validation, effectively addressing diverse business challenges.
 
 </li>
 </ul>
 <li>
 BI, Insights &amp; Business Analysis:
 
 </li>
 <ul>
 <li>
 Act as a Business Intelligence Subject Matter Expert, contributing to the definition of semantic layers, Key Performance Indicators (KPIs), and data models that enable self-service analytics.
 </li>
 <li>
 Guide or develop insightful dashboards and reports using tools like Power BI, Tableau, or Looker, ensuring their actionability and broad adoption across the organization.
 </li>
 <li>
 Perform business analysis, meticulously gathering requirements, mapping processes, and transforming analytical findings into clear recommendations and compelling narratives for various stakeholders.
 
 </li>
 </ul>
 <li>
 Collaboration with Central / Global Teams:
 
 </li>
 <ul>
 <li>
 Prepare and package local or cross-functional solutions, including code, design specifications, documentation, and KPIs, for industrialization and ongoing maintenance by central teams.
 </li>
 <li>
 Contribute to the continuous evolution of global data and AI standards, reference architectures, and best practices.
 </li>
 <li>
 Represent cross-functional analytics and AI needs in global forums, effectively highlighting dependencies, potential risks, and opportunities for scaling solutions.
 
 
 </li>
 </ul>
 </ul>
 Profile
 
 
 We are excited to speak to skilled and experienced data professional with 3+ years of hands-on experience in data science, analytics, or data engineering, demonstrating end-to-end ownership of data products. The ideal candidate will possess strong proficiency in Python (pandas, NumPy, scikit-learn), advanced SQL skills, and experience with cloud data warehouses such as Google BigQuery. Practical experience with enterprise data processing platforms like Dataiku is also essential. Candidates should have demonstrated expertise in at least three of the following areas: data science/machine learning, BI/reporting and metric design, business analysis/stakeholder management, data engineering/data processing, or Gen AI/Agentic AI product deployment. Strong communication and storytelling abilities are crucial for fostering collaboration between business and technical teams.
 
 
 Preferred qualifications include experience in retail, luxury, fashion, or consumer environments, familiarity with experimentation (A/B testing), customer analytics (segmentation, propensity, churn, LTV), or marketing attribution. Exposure to Generative AI/LLMs and their application to customer experience and productivity, along with experience in a hub-and-spoke operating model, are also highly valued. Core competencies for this role include transversal product thinking, technical breadth spanning data science, MLOps, BI, data analysis, and data engineering, the ability to lead initiatives effectively across functions and geographies, and a strong outcome orientation focused on measurable business value and long-term maintainability.
 
 
 The appointed candidate will be offered a base salary within the range USD $140,000 - $160,000, a comprehensive benefits package including: medical, dental, vision, short and long-term disability, various paid time off programs, employee discount/perks and two retirement plans both with employer contributions.
 
 
 <ul>
 <li>
 Please note that restrictions may apply to part-time employees
 
 
 </li>
 </ul>
 
 The position requires 4 days per week on site in the Louis Vuitton Corporate Office, located in the heart of Midtown, Manhattan.
 
 
 
 Additional information
 
 
 Louis Vuitton is a company that respects the uniqueness of each employee and offers everyone the means to find their place and prosper. We promote initiatives aimed at supporting professional equality for everyone. We strive to go above and beyond purely symbolic measures by building a culture passionate about meaningful strategies aimed at crafting an inclusive workforce.
 
 
 In addition to a generous benefits package, unparalleled career development opportunities, both locally &amp; globally, as an employee at Louis Vuitton, you can expect to be provided with industry leading training which will offer you an in-depth insight into the luxury and retail environment.
 
 
 Reference LVM32143
</div>

Senior Data Scientist – Analytics and AI Data

Gambit Technologies

<div>
 
 
 Senior Data Engineer | NYC Hybrid (4 Days per-week Onsite)
 
 
 
 
 
 
 High-impact role. Real ownership. Build something that actually matters.
 
 
 We&rsquo;re partnering with a large, mission-driven organization tackling complex challenges across public impact initiatives. They&rsquo;re investing heavily in modernizing their data platform and are looking for a senior, hands-on engineer to lead the charge.
 
 
 This is not a maintenance role. You&rsquo;ll help define the future state.
 
 
 
 
 
 What You&rsquo;ll Be Doing
 
 
 You&rsquo;ll sit at the intersection of engineering and strategy, helping transition a legacy BI environment into a modern, cloud-native data platform.
 
 <ul>
 <li>
 Design and build a cloud-based data warehouse or lakehouse from the ground up
 </li>
 <li>
 Lead the migration from an on-prem SQL Server BI stack to scalable cloud architecture
 </li>
 <li>
 Build and optimize data pipelines that actually perform at scale
 </li>
 <li>
 Own data modeling, testing, observability, and CI/CD best practices
 </li>
 <li>
 Reduce technical debt and stabilize production systems during the transition
 </li>
 <li>
 Partner directly with business stakeholders to deliver meaningful insights
 </li>
 <li>
 Help define standards and mentor others on the team
 </li>
 </ul>
 
 Current Environment
 
 <ul>
 <li>
 SQL Server (data warehouse)
 </li>
 <li>
 Tableau (primary BI tool)
 </li>
 <li>
 Evolving toward modern cloud data platforms
 </li>
 </ul>
 
 What You Bring
 
 <ul>
 <li>
 Strong hands-on Tableau experience (non-negotiable)
 </li>
 <li>
 Deep SQL skills, including large-scale query optimization
 </li>
 <li>
 Proven experience with data modeling for analytics/reporting
 </li>
 <li>
 Experience working with business stakeholders, not just hiding behind code
 </li>
 <li>
 Ability to translate messy data into clear, actionable insights
 </li>
 </ul>
 
 
</div>

Senior Data Engineer

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

Equinox

<div>Company Description 
OUR STORY:
Equinox Group is a high growth collective of the world's most influential, experiential, and differentiated lifestyle brands. We restlessly seek what is next for maximizing life - and boldly grow the lifestyle brands and experiences that define it. In addition to Equinox, our other brands, SoulCycle and Equinox Hotels are all recognized for inspiring and motivating members and employees to maximize life. Our portfolio of brands is recognized globally with locations within every major city across the United States in addition to London, Toronto, and Vancouver.
OUR CODE:
We are passionate about high performance living and we practice what we preach &ndash; investing time in our own health and fitness. We believe that everyone has untapped potential within them and it takes a disruptive approach to unleash it. We dream big and don&rsquo;t settle for the status quo. We sweat the details. We never accept less than 110% to help each other deliver the Equinox experience and enable our members to get great results. We are obsessed with what&rsquo;s new, what&rsquo;s now, what&rsquo;s next. Never following, always leading, and living ahead of the moment in fashion, culture, and consumer behavior. We aren&rsquo;t just a company; we&rsquo;re a community vested in each other&rsquo;s success. We value humility and a team approach at every level of the company.
If you are a high performing individual who is passionate about winning and inspiring others then we are excited to discuss career opportunities with you. 
Job Description 
Equinox is seeking a Data Engineer to join our technology team. This role is a hands-on position responsible for the build and continued evolution of the data platform, business applications and integration tools. We are looking for a hands on engineer that can recommend best practices to work with enterprise data. The ideal candidate is going to be a hands on engineer, and very strong with AWS Products to help build out data pipelines, create jobs, manage the quality of the data warehouse and integrated tools.
This is a full-time role located in our NYC office.
RESPONSIBILITIES<ul><li>
Design, develop, and maintain scalable, efficient data pipelines to support ETL/ELT processes across multiple sources and systems</li><li>
Partner with Data Science, Analytics, and Business teams to understand data needs, prioritize use cases, and deliver reliable datasets and models</li><li>
Monitor, optimize, and troubleshoot data jobs, ensuring high availability and performance of data infrastructure</li><li>
Build and manage data models and schemas in Redshift and other data technologies, enabling self-service analytics</li><li>
Implement data quality checks, validation rules, and alerting mechanisms to ensure trust in data</li><li>
Leverage AWS services like Glue, Lambda, S3, Athena, and EMR to build modular, reusable data solutions</li><li>
Drive improvements in data lineage, cataloging, and documentation to ensure transparency and reusability of data assets</li><li>
Create and maintain technical documentation and version-controlled workflows (e.g., Git, dbt)</li><li>
Contribute to and promote a culture of continuous improvement, mentoring peers and advocating for scalable and modern data practices</li><li>
Participate in sprint planning, code reviews, and team retrospectives as part of an Agile development process</li><li>
Stay current on industry trends and emerging technologies to identify opportunities for innovation and automation</li></ul> 

Qualifications 
<ul><li>
6+ years of experience in software/data engineering, with a proven track record of building robust data systems (prior leadership or mentorship experience is a plus)</li><li>
Advanced Python, including experience building APIs, scripting ETL processes, and automating workflows</li><li>
Expert in SQL, with ability to write complex queries, optimize performance, and work across large datasets</li><li>
Hands-on experience with AWS data ecosystem including Redshift, S3, Glue, Athena, EMR, EC2, DynamoDB, Lambda, and Redis</li><li>
Strong understanding of data warehousing and data modeling principles (e.g., star/snowflake schema, dimensional modeling)</li><li>
Familiarity with dbt Labs and modern ELT/analytics engineering practices</li><li>
Experience working with structured, semi-structured, and unstructured data</li><li>
Knowledge of data governance, quality assurance, and observability tools to ensure data integrity and trust</li><li>
Proficiency in creating technical documentation, SOPs, and maintaining clear communication with cross-functional teams</li><li>
A problem-solving mindset, with the ability to take ownership and drive solutions from design to deployment</li></ul>
Pay Transparency: $145k - $160k 
Additional Information 
AS A MEMBER OF THE EQUINOX TEAM YOU WILL RECEIVE:<ul><li>
We offer competitive salary, benefits, and industry leading commission opportunities for club employees</li><li>
Complimentary Club membership</li><li>
Perks and incentives with our products and services including Personal Training, Pilates, Spa and Shop</li></ul>
This job description is intended to describe the general requirements for the position. It is not a complete statement of duties, responsibilities or requirements. Other duties not listed here may be assigned as necessary to ensure the proper operations of the department.
Equinox is an equal opportunity employer. For more information regarding our career opportunities, please visit one of our clubs or our website at https://careers.equinox.com/
All your information will be kept confidential according to EEO guidelines. Must have a legal right to work in the United States.</div>

Sr. Data Engineer (ON-SITE)

JPMorganChase

<div>JOB DESCRIPTION 
Design and deliver market-leading technology products in a secure and scalable way as a seasoned member of an agile team
As a Software Engineer III (Machine Learning Platform Engineer) at JPMorgan Chase within the Consumer &amp; Community Banking (CCB) line of business, you serve as a seasoned member of an agile team focused on building, scaling, and maintaining robust machine learning platforms. You will design and deliver trusted, market-leading infrastructure and tools that empower data scientists and ML engineers to develop, deploy, and monitor models efficiently and securely. You are responsible for implementing critical technology solutions across multiple technical areas to support the firm&rsquo;s business objectives and drive innovation in ML platform capabilities.
Job responsibilities<ul><li>
Design, build, and maintain scalable machine learning platforms and infrastructure to support end-to-end ML workflows.</li><li>
Develop and optimize tools for model training, deployment, monitoring, and lifecycle management.</li><li>
Integrate data engineering, feature management, and model serving capabilities into unified ML platform solutions.</li><li>
Implement secure, high-quality production code for platform services, APIs, and automation pipelines.</li><li>
Collaborate with data scientists, ML engineers, and product teams to understand requirements and deliver platform features that accelerate ML development and operations.</li><li>
Ensure platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.</li><li>
Produce architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.</li><li>
Automate infrastructure provisioning, configuration, and CI/CD pipelines for ML platform services.</li><li>
Contribute to the ML platform engineering community of practice and participate in events that explore new and emerging technologies.</li></ul>
Required qualifications, capabilities, and skills<ul><li>
Formal training or certification on software engineering concepts and 3+ years applied experience</li><li>
Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure</li><li>
Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
</li><li>Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL)
</li><li>Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure</li><li>
Strong understanding of MLOps practices, including CI/CD for ML, model versioning, and monitoring</li><li>
Experience developing APIs and platform services for ML workflows
</li><li>Solid knowledge of the software development life cycle and agile methodologies
</li><li>Ability to collaborate with cross-functional teams to deliver platform solutions aligned with business objectives
</li></ul>Preferred qualifications, capabilities, and skills<ul><li>
Familiarity with Databricks for scalable data engineering and ML platform integration</li><li>
Experience working with Snowflake for cloud-based data warehousing and analytics</li><li>
Exposure to Snorkel AI for programmatic data labeling and training data management</li><li>
Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)</li><li>
Familiarity with feature stores, model registries, and ML metadata management</li><li>
Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)</li><li>
Experience with RESTful APIs and microservices architectures</li></ul>
ABOUT US 
Chase is a leading financial services firm, helping nearly half of America&rsquo;s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans 
 
 
ABOUT THE TEAM 
<div>
Our Consumer &amp; Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We&rsquo;re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions &ndash; all while ranking first in customer satisfaction.</div><div> 
 
</div><div>The CCB Data &amp; Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.</div></div>

Software Engineer III - Machine Learning Platform

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