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Data Engineer

Up to $200K + equity New York, NY On-site Finance data AI Series B 51-200 people

About the role

Data Engineer

Comp: $180k - $200k + Equity


Who you'll join


An early-stage fintech building a production-grade data platform for public markets. The data team is small, works close to the product, and takes full ownership of what it ships. If you have spent time at Bloomberg, Palantir, or a buy-side data firm, you will recognise the standard they are aiming for.


What you'll do


  1. Build and run production data pipelines with full operational ownership.
  2. Design schemas that model financial domains and absorb new data sources cleanly.
  3. Resolve entities and map identifiers across vendor feeds, APIs, and scraped sources.
  4. Onboard third-party data including messy vendor feeds, partner APIs, and PDFs.
  5. Monitor data quality using validation, contracts, freshness checks, and lineage tools.
  6. Model reference data covering security masters, corporate actions, and filings.
  7. Instrument pipelines for observability and act on failures without hand-holding.


Who you are


  1. Spent several years running production pipelines with real orchestration ownership.
  2. Write strong SQL and Python without needing scaffolding or close review.
  3. Model data well and can explain why a schema is designed the way it is.
  4. Understand public markets reference data, corporate actions, and identifier standards.
  5. Communicate clearly and flag problems early without being chased.
  6. Work autonomously and take problems from discovery through to production.
  7. Bonus: familiar with streaming or tick data, time-series storage, or knowledge graphs.


Tech stack


Python, SQL, Airflow, Dagster, Prefect, data warehouse or lakehouse (e.g. Snowflake, BigQuery, Databricks), ISIN, CUSIP, SEDOL, FIGI, RIC, Bloomberg, LSEG / Refinitiv, S&P, FactSet, ICE


Why you'll thrive


  1. Work directly with the Head of Data on a small team where your choices matter.
  2. Own your work end to end, from ingestion through to production monitoring.
  3. Public markets data is the core of the product, not a side concern.
  4. Join at a stage where the data architecture is still being defined and built.
  5. Work on-site in New York with a team that holds a high technical bar.