Data Engineer

Open Select; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Open Select is building a production-grade data platform for public markets with a small, highly capable data team that owns end-to-end delivery—from ingestion to production monitoring. You will work on manageable, impactful datasets that power trading decisions.

We seek a data engineer who can run production pipelines, write clean SQL and Python, and model data with clear schema justifications. On-site in New York, you’ll join a team that values autonomy, strong technical craft, and rapid

Qualifications

• Extensive experience running production pipelines with orchestration ownership.
• Proficient in SQL and Python with minimal scaffolding.
• Ability to model data and justify schema decisions.
• Understanding of public markets reference data and identifier standards.
• Clear communication and proactive problem-solving.

Responsibilities

• Build and operate production data pipelines end-to-end.
• Design schemas for financial domains and absorb new data sources cleanly.
• Resolve 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 securities masters, corporate actions, and filings.
• Instrument pipelines for observability and handle failures without hand-holding.

Skills

SQL

Python

Data modeling

Public markets knowledge

Communication

Autonomy

Streaming / tick data (bonus)

Tools

Airflow

Dagster

Prefect

Snowflake

BigQuery

Databricks

Job description

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.

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.