Job description

Want to help everyday Americans invest and build wealth? Financial inequality is increasing, and too many people are getting left behind. At Stash, we’re passionate about democratizing wealth creation through education, advice, and products that help customers achieve greater financial freedom. We also believe in working smarter—leveraging AI and emerging technologies to move faster, operate more efficiently, and focus our time on solving meaningful problems for our customers. We’re looking for a Senior Analytics Engineer (Technical Level 4) to own and evolve the analytics foundation that powers Stash—our dbt-powered data mart , Looker semantic layer, and the quality systems that keep daily numbers trustworthy for Product, Growth, Finance, and Data Science. You’ll sit at the intersection of data engineering and data science: production-grade SQL modeling, testing and freshness, clear metric definitions, and close partnership with stakeholders who depend on self-serve data. Company bets (quality growth, Financial Advice, tier packaging, OKR visibility) all run through the mart—if models break, definitions drift, or sources go stale, the business loses trust. Your job is to make that trust durable. This position operates on a hybrid schedule, requiring you to be on-site in our New York office at least three days per week to work with stakeholders and foster team collaboration. What you'll do: Own core mart domains end-to-end: Design, build, and maintain production dbt models (bronze → silver → gold patterns in our data mart) for high-priority domains such as subscriptions, promotions/attribution, acquisition, and product usage. Raise reliability and quality: Drive down recurring dbt test failures; add meaningful tests; document exceptions; partner on freshness SLAs and alerting so stale or wrong data is caught before Looker, OKRs, or DS models. Keep Eng and the mart aligned: Partner with Backend / Product Engineering on instrumentation and schema changes (e.g., service migrations). Reconcile parity, get stakeholder sign-off, and cut over without silent metric breaks. Enable Data Science and self-serve: Turn DS modeling requests into governed mart objects (grains, definitions, consumers). Build Looker explores/views and documentation so analysts and PMs can answer questions without waiting on a ticket for every pull. Improve ops and performance: Contribute to mart job reliability (retries for transient failures, clear ownership of non-retryable logic failures). Profile and refactor high-cost models when reliability work is on track. Partner across Data: Work with Data Engineering on upstream contracts and ingestion quality; with Data Science on measurement-ready datasets; with stakeholders on metric definitions that stick. Raise the bar for the team: Mentor peers, review PRs for modeling and test quality, and use AI coding assistants productively while owning correctness—especially around financial and customer data. What we're looking for: Experience: 5+ years in analytics engineering, data engineering (analytics-focused), or closely adjacent roles building production analytical data models. Evidence of Senior / L4-equivalent ownership of critical reporting domains. dbt & SQL craft: Advanced SQL and production-grade dimensional / mart modeling. Deep dbt experience (models, tests, sources, docs, incremental strategies, performance tradeoffs)—not “I’ve written a few models.” Warehouse experience: Hands-on with a modern cloud warehouse ( Redshift , dbt, DataFold). Comfort debugging joins, grains, late-arriving data, and cost/runtime. BI / semantic layer: Experience exposing trusted metrics in Looker and caring about naming, descriptions, and explore usability. Quality mindset: You’ve owned data quality incidents—root cause, stakeholder communication, and durable fixes (tests, contracts, runbooks)—not just hotfixes. Collaboration: Strong partnership with engineers, data scientists, and business stakeholders; able to negotiate definitions and push back when a request would create an unmaintainable model. Programming: Solid Python for analysis, tooling, and light automation; Git/PR workflows are second nature. Education: Bachelor’s in a quantitative or technical field, or equivalent experience. AI fluency: Proven hands-on use of AI tools (e.g. Cursor, ChatGPT) in daily workflows, with strong judgment—validating outputs, adhering to Stash guidelines for sensitive data, and owning the quality of AI-assisted work. Gold Stars: Experience in fintech, subscriptions, or regulated environments (PCI / SOC 2 awareness). Mixpanel / Segment (or similar) event modeling experience. Prior work enabling ML / DS feature tables or experiment assignment grains in the warehouse. Mentorship or informal tech-lead experience on an AE / DE squad. Familiarity with CI/CD on Github actions. #LI-Hybrid Our Commitment to Diversity, Equity, and Inclusion We proudly celebrate the unique qualities that make you you, 365 days a year, and not just because it’s the right thing to do or good for business. We embed the principles and practices of diversity, equity, and inclusion (DEI) into all that we do to prioritize people, a Stash core value , and to ensure Stashers of all backgrounds and experiences can be their authentic selves. We are also proud to be the first and only venture-backed fintech to join the CEO Action for Diversity & Inclusion™ , and as an Equal Opportunity Employer, Stash is committed to building an inclusive environment for people of all backgrounds. If you require any reasonable accommodations to make your application process more accessible, please reach out to recruiting@Stash.com. Helping You Invest in Yourself Comprehensive total rewards package, comprising compensation (salary and equity) and health care benefits Complimentary subscription to Stash+ account Flexible work policy – We offer a flexible work environment that blends working from home with in-person collaboration at our NYC office to support productivity and team culture. Flexible PTO Annual learning and development reimbursement benefit Work-from-home equipment stipends; home internet subsidy Paid Parental Leave (offerings for birth giving and non-birth giving parents) Primary & Secondary Enhanced health and wellness benefits through One Medical, Gympass, and Maven Health External Recognition for Stash Benzinga’s 2023 Best Brokerage for Beginners and Best Robo-Advisor Awards Qorus-Accenture’s 2023 Banking Innovation Awards USA Today and Statista’s 2023 Top 500 Best Financial Advisory Firms Comparably's Best Company Awards: Best Places to Work, Best Company Outlook, and Best Engineering Team for Diversity, Women, Culture, and more! (2023) Fintech Breakthrough Award: Best Personal Finance App (2023) BuiltIn’s Best Places to Work (2022, 2021, 2020, 2019) Forbes Fintech 50 (2021, 2020, 2019) Best Digital Bank, Finovate Awards (2020) Tearsheet Challenge Awards, Best Banking Card Product - Stock-Back® Card, 2020 LendIt Fintech Innovator of the Year (2020, 2019) Salary Range: $150,000 - $180,000 The base salary range represents the reasonably anticipated low and high end of the salary range for this position. Actual salaries will vary and will be based on various factors, such as the candidate’s qualifications, skills, experience and competencies, as well as internal equity and alignment with market data for companies of our size and industry. **No recruiters, please**

Requirements

Must-haves

This position operates on a hybrid schedule, requiring you to be on-site in our New York office at least three days per week to work with stakeholders and foster team collaboration.
Raise reliability and quality: Drive down recurring dbt test failures; add meaningful tests; document exceptions; partner on freshness SLAs and alerting so stale or wrong data is caught before Looker, OKRs, or DS models.
Improve ops and performance: Contribute to mart job reliability (retries for transient failures, clear ownership of non-retryable logic failures). Profile and refactor high-cost models when reliability work is on track.
Partner across Data: Work with Data Engineering on upstream contracts and ingestion quality; with Data Science on measurement-ready datasets; with stakeholders on metric definitions that stick.
Raise the bar for the team: Mentor peers, review PRs for modeling and test quality, and use AI coding assistants productively while owning correctness—especially around financial and customer data.
dbt & SQL craft: Advanced SQL and production-grade dimensional / mart modeling. Deep dbt experience (models, tests, sources, docs, incremental strategies, performance tradeoffs)—not “I’ve written a few models.”
Warehouse experience: Hands-on with a modern cloud warehouse ( Redshift , dbt, DataFold). Comfort debugging joins, grains, late-arriving data, and cost/runtime.
BI / semantic layer: Experience exposing trusted metrics in Looker and caring about naming, descriptions, and explore usability.
Quality mindset: You’ve owned data quality incidents—root cause, stakeholder communication, and durable fixes (tests, contracts, runbooks)—not just hotfixes.
Collaboration: Strong partnership with engineers, data scientists, and business stakeholders; able to negotiate definitions and push back when a request would create an unmaintainable model.
Programming: Solid Python for analysis, tooling, and light automation; Git/PR workflows are second nature.
Education: Bachelor’s in a quantitative or technical field, or equivalent experience.
AI fluency: Proven hands-on use of AI tools (e.g. Cursor, ChatGPT) in daily workflows, with strong judgment—validating outputs, adhering to Stash guidelines for sensitive data, and owning the quality of AI-assisted work.
Experience in fintech, subscriptions, or regulated environments (PCI / SOC 2 awareness).
Mixpanel / Segment (or similar) event modeling experience.
Prior work enabling ML / DS feature tables or experiment assignment grains in the warehouse.
Mentorship or informal tech-lead experience on an AE / DE squad.
Familiarity with CI/CD on Github actions.
Our Commitment to Diversity, Equity, and Inclusion
If you require any reasonable accommodations to make your application process more accessible, please reach out to recruiting@Stash.com.
Helping You Invest in Yourself
Complimentary subscription to Stash+ account
Flexible work policy – We offer a flexible work environment that blends working from home with in-person collaboration at our NYC office to support productivity and team culture.
Flexible PTO

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlgopythonmlci/cd

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Pto

Parental Leave

Wellness