Data Engineer

Job Description

Data Engineer

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Location: New York, NY (On-site)

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Comp: $180k - $200k + Equity

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Industry: FinTech / AI

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Who you'll join

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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.

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What you'll do

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

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

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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

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Why you'll thrive

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