Data Engineer

Job Description

Data Engineer

Location: New York, NY (On-site)

Comp: $180k - $200k + Equity

Industry: FinTech / AI

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 a buy-side data firm, you will recognize the standard they are aiming for.

What you'll do

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

Who you are

• Spent several years running production pipelines with real orchestration ownership.
• Write strong SQL and Python without needing scaffolding or close review.
• Model data well and can explain why a schema is designed the way it is.
• Understand public markets reference data, corporate actions, and identifier standards.
• Communicate clearly and flag problems early without being chased.
• Work autonomously and take problems from discovery through to production.
• 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

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