Senior Data Engineer

About the company

Our customer is an AI company in investment research, building an intelligence platform for investors and enterprise strategy teams. Some of the world’s largest funds and companies rely on it to make decisions.

They’re growing fast, and the product only works if the data underneath it is right every time.

Galaxy works with their team on data infrastructure. Apply here and we’ll make a direct intro.

The Role

They’re hiring their first dedicated Data Engineer, and you’ll own all of it. That covers the pipelines that feed their AI systems, the models that turn unstructured data into structured, searchable insight, and the systems that will let customers plug their own data into the platform.

You’ll work day to day with their applied AI, research, and engineering teams, but the architecture calls are yours. Their clients make investment decisions on this data, so accuracy isn’t a feature. It’s the product. You’ll set that bar and shape how data works there for years.

You will…

• Build and run ETL/ELT pipelines that bring unstructured text, quantitative metrics, and third party datasets into core systems

• Design data models across Postgres, S3, and semantic search, picking the right store for each job

• Build LLM-powered enrichment pipelines that extract, classify, and structure data, with evals that keep them honest

• Own data quality end to end, with automated checks and alerting that catch problems before clients do

• Resolve company entities across sources, including identifiers, subsidiaries, hierarchies, and acquisitions

• Define schemas and data contracts with research, AI, and engineering as new features ship

• Document lineage, schemas, and runbooks as you build

You have…

• Strong SQL and a feel for how data behaves at scale

• Production experience in Python or TypeScript

  • Their pipelines run on TypeScript today

• Experience building reliable integrations with APIs, databases, and files, handling auth, pagination, rate limits, incremental syncs, retries, and schema drift

• Comfort with structured, semi-structured, and unstructured data, and the tradeoffs between relational, object, search, and analytical stores

• A first principles approach and energy for turning messy data into something solid

Bonus points if you…

• Have built LLM-driven pipelines for labeling, moderation, or quality scoring

• Have done entity resolution across messy company data

• Were early on a data or engineering team and built the function from scratch

• Have worked in fintech, financial services, or research and data products

• Have used Postgres-native queues, AWS Bedrock, or a warehouse stack like Snowflake, BigQuery, dbt, or Airflow

Details

• Location

  • NYC, in person

• Compensation

  • $170,000-$200,000 base, plus a performance bonus and meaningful equity

• Benefits

  • Health coverage

  • Flexible PTO

  • Paid holidays