analytics engineer

V-Soft Consulting Group, Inc.

<div>
 
 Primary Location
 
 : New York City, New York
 
 
 V-Soft Consulting is currently hiring for a
 
 Data Engineer
 
 for our premier client in
 
 
 
 New York City, New York.
 
 
 
 
 Education And Experience &raquo;
 
 
 
 B.S. or B.A in Computer Science or related field.
 
 
 <ul>
 <li>
 5&ndash;7 years in data engineering or analytics engineering.
 </li>
 <li>
 Experience operating data ware/lake-houses and building semantic layers.
 </li>
 <li>
 Experience with external client reporting, embedded analytics, and multi-tenant considerations (modeling, partitioning, access controls).
 
 
 </li>
 </ul>
 
 Knowledge, Skills And Abilities &raquo;
 
 
 
 <ul>
 <li>
 Strong SQL and practical experience with dbt for transformations and testing.
 </li>
 <li>
 Hands-on experience with data modeling and performance optimization.
 </li>
 <li>
 Professional coding experience with both Java and Python.
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Cost/performance tuning across data ware/lake-housing, query engines, BI tools; caching strategies and aggregation design.
 </li>
 <li>
 Data documentation, lineage, and cataloging practices; strong habits around reproducibility and testing.
 </li>
 <li>
 Apply report design and visualization best practices (effective layouts, clear metric definitions, consistent interactions, and performant SQL+DAX queries) to deliver executive ready dashboards.
 </li>
 <li>
 Experience with any of the following: Azure Fabric, Power BI, Dremio, Apache Iceberg, Apache Parquet, Datadog
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Do
 
 
 
 
 Job Responsibilities:
 
 
 
 <ul>
 <li>
 Demonstrated ability to mentor or coach in software engineering practices.
 </li>
 <li>
 Effective communication and requirement gathering
 
 
 </li>
 </ul>
 
 Interested?
 
 
 
 Qualified candidates should send their resumes to
 
 rprudhvi@vsoftconsulting.com
 
 
 
 V-Soft Consulting Group is recognized among the top 100 fastest growing staffing companies in North America, V-Soft Consulting Group is headquartered in Louisville, KY with strategic locations in India, Canada and the U.S. V-Soft is known as an agile, innovative technology services company holding several awards and distinctions and has a wide variety of partnerships across diverse technology stacks.
 
 
 As a valued V-Soft Consultant, you&rsquo;re eligible for full benefits (Medical, Dental, Vision), a 401(k) plan, competitive compensation and more. V-Soft is partnered with numerous Fortune 500 companies, exceptionally positioned to advance your career growth.
 
 
 V-Soft Consulting provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
 
 
 For more information or to view all our open jobs, please visit www.vsoftconsulting.com or call (844) 425-8425.
 
 
</div>

Data Engineer

ALICE + OLIVIA

<div>
 
 Location: New York, NY or Secaucus, NJ (Hybrid)
 
 
 Decision Science Engineer (Applied Data Scientist)
 
 
 
 
 
 
 POSITION SUMMARY:
 
 
 
 ALICE + OLIVIA is hiring a Decision Science Engineer to build, operationalize, and own predictive models that improve decision making across Planning, Merchandising/Buying, Customer 360, and Inventory Management. Working primarily within Palantir Foundry as the core analytics and deployment platform, this role bridges data science and platform engineering, translating real-world commercial constraints including seasonality, product lifecycle, allocations, replenishment, promotions/markdowns, and customer behavior into practical, production-grade, human-in-the-loop models.
 
 
 
 
 
 This is a highly cross-functional role with direct, ongoing exposure to business stakeholders and end users, from planners and buyers who consume and act on model outputs, to the teams working within Foundry Workshop applications day-to-day. Understanding how outputs are used in real workflows, and staying close to the people using them, is central to how this role operates. Sitting within the IT organization and serving the full business including DTC, wholesale, planning, and supply chain, this position carries direct visibility to executive business stakeholders. This role is focused on applied data science, decision-support modeling, and model operationalization, not reporting or core data engineering, while partnering closely with Engineering, BI, and the broader Enterprise Applications team.
 
 
 
 
 
 
 RESPONSIBILITIES:
 
 
 <ul>
 <li>
 Design, build, and maintain predictive and statistical models supporting retail planning, buying, inventory, and demand forecasting.
 </li>
 <li>
 Operationalize models within Palantir Foundry using Code Workbook and Code Repository, working against Snowflake as the primary data source and ensuring outputs are production-grade and maintainable.
 </li>
 <li>
 Own and evolve analytics logic and assumptions embedded in planning and buying workflows.
 </li>
 <li>
 Partner with Planning, Buying, Merchandising, and DTC teams to translate business needs into model-driven insights, maintaining direct, ongoing relationships with the stakeholders and end users consuming model outputs.
 </li>
 <li>
 Collaborate with the Enterprise Applications team on Workshop app development to ensure model outputs are surfaced effectively in business-facing Foundry applications.
 </li>
 <li>
 Build models for customer segmentation, CLV, retention/churn, propensity, and campaign targeting (Customer 360).
 </li>
 <li>
 Collaborate with Engineering, who owns data pipelines and Snowflake ingestion, to ensure model inputs are reliable, well-governed, and fit for purpose without owning the underlying data infrastructure.
 </li>
 <li>
 Support human-in-the-loop workflows where planners and buyers can interrogate, override, or adjust model outputs in real time.
 </li>
 <li>
 Validate model outputs against business reality and historical performance, closing the loop directly with end users on model accuracy and relevance.
 </li>
 <li>
 Document methodologies and assumptions to support long-term ownership and institutional knowledge.
 </li>
 </ul>
 
 
 
 
 
 QUALIFICATIONS:
 
 
 <ul>
 <li>
 4 to 8+ years of experience in data science, applied analytics, or predictive modeling.
 </li>
 <li>
 Strong experience building predictive or statistical models including forecasting, optimization, regression, and classification.
 </li>
 <li>
 Proficiency in Python for data analysis, modeling, and production-grade code.
 </li>
 <li>
 Strong SQL and hands-on experience with Snowflake or equivalent cloud data warehouse.
 </li>
 <li>
 Experience with Palantir Foundry strongly preferred, including familiarity with the Foundry Ontology layer, analytics and modeling tooling (Code Workbook, Code Repository), and how model outputs are surfaced through business-facing applications. Candidates who can reason within and contribute to an existing Ontology, and who understand how the semantic data layer connects to model design, will be prioritized.
 </li>
 <li>
 Familiarity with experiment design and causal inference.
 </li>
 <li>
 Experience with time-series forecasting at scale.
 </li>
 <li>
 Retail domain experience strongly preferred, including familiarity with demand vs net sales, sell-through, markdowns, planning vs buying workflows, seasons, price groups, and store vs e-commerce dynamics.
 </li>
 <li>
 Experience working with messy, real-world data and designing models that support business judgment.
 </li>
 <li>
 Ability to reason about semantic or ontology-based data models.
 </li>
 <li>
 Strong communication and relationship skills, comfortable working directly with planners, buyers, and business stakeholders, and able to translate model outputs into decisions, not just insights.
 </li>
 <li>
 Long-term ownership mindset for maintaining and evolving models over time.
 </li>
 <li>
 Hands-on experience building or contributing to Palantir Foundry Workshop applications.
 </li>
 <li>
 Background in wholesale or multi-channel retail, including familiarity with the operational differences between DTC, wholesale, and brick-and-mortar planning cycles.
 </li>
 <li>
 Exposure to agent-based or AI-assisted analytics workflows.
 </li>
 <li>
 Experience working within a formal data governance or steerco-driven prioritization structure.
 </li>
 <li>
 Familiarity with Snowflake-native development patterns including dbt, Snowpark, or similar.
 </li>
 </ul>
 
 
 
 
 
 ADDITIONAL INFORMATION:
 
 
 
 This role sits within the IT organization, reporting to the VP of Information Technology, and operates across the full business including Planning, Merchandising/Buying, DTC, and Supply Chain. Engineering owns ETL, data pipelines, and Snowflake ingestion. BI owns reporting and dashboards. This role owns predictive modeling, decision-support analytics, and model operationalization within Palantir Foundry. Work is prioritized through a cross-functional steering committee with direct business stakeholder representation, ensuring this role has structured visibility into commercial priorities and executive-level exposure from day one.
 
 
 
 
 
 ALICE + OLIVIA is at an inflection point in how it uses data to make decisions. This role is central to that transformation, building the analytics capability that connects our Palantir Foundry investment to real commercial outcomes across every part of the business. This is not a role that maintains existing infrastructure or produces reports. It is a greenfield opportunity to own a strategic function, work directly with the teams making decisions, and build something that compounds in value over time. For the right person, it is a rare combination of technical depth, business proximity, and organizational support.
 
 
 
 
 
 $150,000 -175,000 base salary (This range represents the low and high end of the anticipated base salary range for this NY OR NJ - based position. The base salary will depend on numerous factors such as: experience and qualifications for the role, experience level, skillset, and balancing internal equity relative to other A+O employees.)
 
</div>

Data Scientist

Shields Group Search

<div>
 
 
 Data Engineer
 
 
 
 
 Hybrid in NYC
 
 
 
 
 Full-Time
 
 
 
 
 $175,000-$225,000 base + upside depending on experience
 
 
 
 
 
 
 
 About the Company
 
 
 
 Shields Group Search is partnering with an innovative private banking and wealth infrastructure company building modern tools for modern clients. Their platform is designed to help private investors access liquidity more easily while driving transparency into historically opaque markets, including fine art and commercial real estate.
 
 
 
 
 
 They are hiring a
 
 Data Engineer
 
 to help build a world-class data infrastructure that supports the modernization of private banking for independent wealth managers and family offices. This person will be responsible for designing, building, and maintaining robust data pipelines, ETL processes, and data warehousing solutions that enable analytics and drive insights across the platform.
 
 
 
 
 
 You will work closely with engineering and product teams to architect scalable, reliable data systems. The stack includes
 
 Python, SQL, cloud data warehousing platforms, and modern data engineering tooling
 
 .
 
 
 
 
 
 
 About the Role
 
 
 
 This position reports directly to the
 
 Head of Engineering
 
 . The ideal candidate will have meaningful educational and/or professional experience designing and implementing scalable data systems, building ETL pipelines, and writing clean, maintainable, well-tested code.
 
 
 
 
 
 This team is looking for someone who is genuinely excited to spend most of their day coding and building scalable systems, while also contributing architectural input. They would strongly prefer someone in
 
 NYC
 
 , and
 
 2-3 days per week in-office
 
 should be expected. They also want someone who is comfortable building with and using
 
 agentic systems
 
 . This is not the right fit for someone optimizing primarily for work/life balance.
 
 
 
 
 
 
 Responsibilities
 
 
 <ul>
 <li>
 Participate in the full software development life cycle from planning to deployment
 </li>
 <li>
 Design and build scalable ETL/ELT pipelines to ingest, transform, and load data from multiple sources
 </li>
 <li>
 Collaborate with the team to design, develop, unit test, and support new features
 </li>
 <li>
 Strive for high quality and rapid output through agile software development best practices
 </li>
 <li>
 Architect data warehouses and data lakes, optimizing for query performance and cost efficiency
 </li>
 <li>
 Perform code reviews and provide constructive feedback to peers
 </li>
 <li>
 Maintain and improve the tech stack
 </li>
 <li>
 Develop flexible and well-structured data pipelines and data warehouse schemas
 </li>
 <li>
 Implement effective security protocols, data protection measures, and storage solutions
 </li>
 <li>
 Troubleshoot, debug, and upgrade software
 </li>
 <li>
 Ensure software meets all requirements of quality, security, and configurability
 </li>
 <li>
 Contribute to documentation, tests, and automation pipelines
 </li>
 </ul>
 
 
 
 
 
 Requirements
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Software Engineering, or a related field
 </li>
 <li>
 Minimum of 3 years of experience in data engineering, ETL development, or analytics engineering, ideally within fintech or financial services
 </li>
 <li>
 Advanced proficiency in Python and SQL for data pipeline development and analysis
 </li>
 <li>
 Familiarity with CI/CD tools
 </li>
 <li>
 Experience with data warehousing platforms such as Snowflake, BigQuery, Redshift, or similar, as well as modern ETL tools
 </li>
 <li>
 Strong knowledge of the software development lifecycle
 </li>
 <li>
 Experience with testing and debugging
 </li>
 <li>
 Proficient understanding of code versioning tools such as Git
 </li>
 <li>
 Excellent problem-solving skills and attention to detail
 </li>
 <li>
 Excellent communication and teamwork skills
 </li>
 <li>
 Ability to work independently and take initiative
 </li>
 <li>
 Willingness and ability to build with agentic systems in a practical, hands-on way
 </li>
 </ul>
 
 
 
 
 
 Why This Role
 
 
 
 This is an opportunity to help architect a state-of-the-art platform and partner with an experienced leadership team during a period of rapid development. The team has additional flexibility on compensation for stronger candidates, with total base salary landing closer to
 
 $200,000-$225,000
 
 for candidates with more experience.
 
</div>

The Carlyle Group

<div>
 
 
 Company Profile
 
 
 
 The Carlyle Group (NASDAQ: CG) is a global investment firm with $475 billion of assets under management, across 678 investment vehicles as of March 31, 2026. Founded in 1987 in Washington, DC, Carlyle has grown into one of the world's largest and most successful investment firms, with more than 2,500 professionals operating in 28 offices in North America, Europe, the Middle East, Asia and Australia.
 
 
 
 
 
 Carlyle's purpose is to connect people, ideas, and capital to fuel growth for companies and performance for investors, which range from public and private pension funds to wealthy individuals and families to sovereign wealth funds, unions and corporations. Carlyle invests across three segments - Global Private Equity, Global Credit and Carlyle AlpInvest - and has deep expertise across industries, markets, and geographies.
 
 
 
 
 
 At Carlyle, we believe that a wide spectrum of experiences and viewpoints drives performance and success. Our CEO, Harvey Schwartz, has stated that, "To build better businesses and create value for all of our stakeholders, we are focused on assembling leadership teams with the strongest insights from a range of perspectives." Reflecting this view, emphasis is placed on development, retention and inclusion through our internal processes and seven Employee Resource Groups (ERGs). We cultivate a culture where ideas are openly shared and challenged, connecting diverse expertise and perspectives to drive enduring value.
 
 
 
 
 
 
 Fund or Department Description
 
 
 
 The Data &amp; AI Engineer sits within Carlyle's Enterprise Technology &amp; Data organization and supports firm-wide data and AI initiatives spanning investment platforms, portfolio operations, investor relations, and corporate functions. The role operates within a federated data operating model, partnering with domain engineering teams to implement shared platforms and reusable patterns for data and AI under the technical direction of the Senior AI &amp; Data Architect.
 
 
 
 
 
 
 Position Summary
 
 
 
 The Data &amp; AI Engineer is an experienced, hands-on engineer who turns Carlyle's data and AI architecture into working production systems. Reporting to the Senior AI &amp; Data Architect, this role is responsible for building and operating the pipelines, semantic layers, retrieval systems, and AI-ready data products that power analytics, automation, LLMs, agents, and generative AI applications across the firm.
 
 
 
 
 
 The role requires deep, hands-on expertise across modern data engineering and applied AI engineering. The Data &amp; AI Engineer will implement retrieval-augmented generation (RAG) patterns, embedding and indexing pipelines, vector stores, and semantic models alongside core ELT, streaming, and analytical pipelines - treating LLMs, agents, and copilots as first-class consumers of the data platform.
 
 
 
 
 
 This is a senior individual-contributor engineering role that executes against architectural standards, contributes to their evolution through hands-on learning, and partners closely with data science, AI engineering, governance, and domain teams to deliver trusted, AI-consumable data at enterprise scale.
 
 
 
 
 
 
 What Success Looks Like:
 
 In the first 12 months, this role will deliver foundational AI-ready data pipelines and retrieval components defined in the target-state architecture, productionize one or more priority RAG or agent-grounding use cases, and establish reusable engineering patterns that other domain teams can adopt across the federated data platform.
 
 
 
 
 
 
 In-office requirement:
 
 4 days per week
 
 
 
 
 
 
 Primary Responsibilities:
 
 
 
 
 AI Data Pipelines &amp; Retrieval Systems (&asymp;35%)
 
 
 <ul>
 <li>
 Build and operate AI-ready data pipelines - embedding generation, chunking, indexing, and refresh workflows - that make Carlyle's enterprise data reliably retrievable by LLMs, agents, and generative AI applications.
 </li>
 <li>
 Implement retrieval-augmented generation (RAG) components, including vector store integrations, hybrid search, re-ranking, and grounding logic, against architectural patterns defined by the Senior AI &amp; Data Architect.
 </li>
 <li>
 Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks.
 </li>
 <li>
 Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products.
 </li>
 <li>
 Contribute to semantic and context engineering work that powers natural-language analytics, conversational reporting, and AI-driven insights for business users.
 </li>
 </ul>
 
 
 
 
 
 Modern Data Pipeline Engineering (&asymp;30%)
 
 
 <ul>
 <li>
 Design, build, and maintain production-grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake.
 </li>
 <li>
 Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency.
 </li>
 <li>
 Write clean, well-tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing.
 </li>
 <li>
 Productionize new sources and domains under the federated operating model, partnering with domain data engineers to apply shared platform capabilities consistently.
 </li>
 </ul>
 
 
 
 
 
 Semantic Layer &amp; Data Product Development (&asymp;20%)
 
 
 <ul>
 <li>
 Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self-service analytics and reliable AI grounding.
 </li>
 <li>
 Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers.
 </li>
 <li>
 Collaborate with the Senior AI &amp; Data Architect to refine and extend enterprise semantic standards based on what works in production.
 </li>
 <li>
 Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction.
 </li>
 </ul>
 
 
 
 
 
 Data Quality, Observability &amp; AI Trust (&asymp;10%)
 
 
 <ul>
 <li>
 Implement data quality checks, lineage capture, and pipeline observability across both data and AI workloads.
 </li>
 <li>
 Build logging, evaluation, and monitoring components for AI systems - including prompt and response capture, retrieval metrics, and model performance signals - in line with governance standards.
 </li>
 <li>
 Partner with Data Governance to operationalize metadata, stewardship, and access controls, ensuring AI systems consume enterprise data with the same rigor as human users.
 </li>
 <li>
 Surface issues early, propose remediations, and feed lessons learned back into architectural patterns.
 </li>
 </ul>
 
 
 
 
 
 Collaboration &amp; Engineering Craft (&asymp;5%)
 
 
 <ul>
 <li>
 Participate in architectural design reviews and contribute hands-on engineering perspective to evolving patterns and standards.
 </li>
 <li>
 Mentor junior data engineers and analysts on modern data and AI engineering practices.
 </li>
 <li>
 Document patterns, write runbooks, and share knowledge across the federated organization to accelerate adoption of reusable platform capabilities.
 </li>
 </ul>
 
 
 
 
 
 Requirements:
 
 
 
 
 Education &amp; Certifications
 
 
 <ul>
 <li>
 Bachelor's degree, required
 </li>
 <li>
 Concentration in computer science, data engineering, information systems, or a related field, preferred
 </li>
 <li>
 Masters degree, preferred
 </li>
 <li>
 Relevant certifications in cloud, data engineering, analytics, or AI/ML are preferred
 </li>
 </ul>
 
 
 
 
 
 Professional Experience
 
 
 <ul>
 <li>
 6+ years of overall relevant technical experience, required
 </li>
 <li>
 Experience in data engineering, analytics engineering, or platform engineering, with at least 1-2 years of direct, hands-on experience building generative AI or AI/ML systems in production.
 </li>
 <li>
 Proven experience implementing retrieval, grounding, and semantic components for LLM- or agent-based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use.
 </li>
 <li>
 Hands-on experience with one or more modern AI platforms and tooling categories (e.g., AWS Bedrock, Databricks ML, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex or equivalents, MLflow, and vector databases such as Databricks Vector Search, pgvector, or Pinecone).
 </li>
 <li>
 Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark).
 </li>
 <li>
 Deep, hands-on experience with modern data stacks - dbt, Fivetran, Snowflake - in AWS-based environments.
 </li>
 <li>
 Track record of building data pipelines and products whose consumers include AI systems, not only BI tools and human analysts.
 </li>
 <li>
 Palantir experience a plus.
 </li>
 <li>
 Experience operating within federated data operating models and complex, regulated enterprise environments; financial services experience preferred.
 </li>
 </ul>
 
 
 
 
 
 Competencies &amp; Attributes
 
 
 <ul>
 <li>
 Demonstrated AI-forward instinct: defaults to asking how AI changes what gets built, rather than whether AI can be added later.
 </li>
 <li>
 Fluency in current AI engineering patterns (RAG, agents, tool use, evaluations, guardrails, observability) and the practical trade-offs involved in shipping them.
 </li>
 <li>
 Strong engineering craft: clean code, automated testing, thoughtful design, and a bias toward production-quality systems over prototypes.
 </li>
 <li>
 Pragmatic, delivery-oriented mindset with strong attention to data quality, AI trust, and long-term maintainability; able to distinguish durable engineering decisions from AI hype.
 </li>
 <li>
 Collaborative partner to architects, data scientists, AI engineers, and domain teams; comfortable operating in a matrixed, federated organization and in high-visibility transformational initiatives.
 </li>
 </ul>
 
 
 
 
 
 Benefits/Compensation
 
 
 
 The compensation range for this role is specific to the applicable office location and takes into account a wide range of factors, including required and preferred skill sets; prior experience and training; and licenses and/or certifications.
 
 
 
 
 
 The anticipated base salary range for this role is $160,000 to $180,000.
 
 
 
 
 
 In addition to base salary, the hired professional will receive a comprehensive benefits package including retirement benefits, health insurance, life and disability insurance, paid time off, paid holidays, family planning benefits, and wellness programs. The hired professional may also be eligible for an annual discretionary incentive program based on individual and organizational performance.
 
</div>

Data & AI Engineer

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>

Data Engineer, Product Analytics

Figma

<div>
 Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma&rsquo;s platform helps teams bring ideas to life&mdash;whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us!
 
 
 We are looking for an experienced Data Engineer to partner with our Data Science and Data Infrastructure teams to own and scale our data pipelines. You&rsquo;ll also work closely with stakeholders across business teams including sales, marketing, and finance to ensure that the data they need arrives promptly and reliably. You&rsquo;ll play an integral role in building the metrics and self-serve reporting capabilities to unlock Figma&rsquo;s next phase of growth.
 
 
 This is a great role for an individual who is passionate about working with data and data systems, and who loves solving problems. You&rsquo;ll have a good sense for when it makes sense to build fast, scrappy solutions to unblock a key stakeholder vs. when to push back or bring in an outside service. The ideal candidate will be a great communicator who can help coordinate across multiple internal and external teams and takes pride in building end-to-end projects.
 
 
 This is a full time role that can be held from one of our US hubs or remotely in the United States.
 
 
 
 What you'll do at Figma
 
 
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 Own, build, and maintain scalable data pipelines that connect various cloud data sources.
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 Develop a deep understanding of Figma&rsquo;s core data models and optimize data pipelines for scale.
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 Partner with the Data Science and Data Infrastructure teams to build new foundational data sets that are trusted, well understood, and enable self-service.
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 Work with a wide range of cross-functional stakeholders to derive requirements and architect shared datasets; ability to document, simplify and explain complex problems to different types of audiences.
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 Establish best practices for the development of specialized data sets for analytics and modeling.
 
 
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 We'd love to hear from you if you have:
 
 
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 4+ years in a relevant field.
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 Fluency with both SQL and Python.
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 Familiarity with Snowflake, dbt, Dagster, and ETL/reverse ETL tools.
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 Excellent judgment and creative problem solving skills.
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 A self-starting mindset along with strong communication and collaboration skills.
 
 
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 While not required, it&rsquo;s an added plus if you also have:
 
 
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 Knowledge in data modeling methodologies to design and build robust data architectures for insightful analytics.
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 Experience with business systems such as Salesforce, Customer IO, Stripe, NetSuite is a big plus.
 
 
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 At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you&rsquo;re excited about this role but your past experience doesn&rsquo;t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
 
 
 
 Pay Transparency Disclosure
 
 
 
 If based in Figma&rsquo;s San Francisco or New York hub offices, this role has the annual base salary range stated below.
 
 
 Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. The listed range is a guideline, and the range for this role may be modified. For roles that are available to be filled remotely, the pay range is localized according to employee work location by a factor of between 80% and 100% of range. Please discuss your specific work location with your recruiter for more information.
 
 
 Figma offers equity to employees, as well a competitive package of additional benefits, including health, dental &amp; vision, retirement with company contribution, parental leave &amp; reproductive or family planning support, mental health &amp; wellness benefits, generous PTO, company recharge days, a learning &amp; development stipend, a work from home stipend, and cell phone reimbursement. Figma also offers sales incentive pay for most sales roles and an annual bonus plan for eligible non-sales roles. Figma&rsquo;s compensation and benefits are subject to change and may be modified in the future.
 
 
 Annual Base Salary Range:
 
 
 $140,000&mdash;$348,000 USD
 
 
 At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status
 
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 or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
 
 
 We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com. These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities.
 
 
 Examples of accommodations include but are not limited to:
 
 
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 Holding interviews in an accessible location
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 Enabling closed captioning on video conferencing
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 Ensuring all written communication be compatible with screen readers
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 Changing the mode or format of interviews
 
 
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 To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding.
 
 
 By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice.
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