Data Engineer

Gambit Technologies; New Haven (CT); On-site; USD 110,000 - 150,000; Full time

Job summary

Gambit Technologies in Connecticut seeks a mid-level Data Engineer to help build and scale a modern data platform for an institutional investment office. You’ll own data domains end to end, from ingestion and modeling to quality checks and delivery to analysts.

You will collaborate with investors and the data team to shape data architecture, automate pipelines, and implement reusable views used in portfolio analytics and investment memos.

Qualifications

• 3–6 years as a data engineer on a modern data platform.
• Proficient in Python and SQL with production quality.
• Experience with Databricks, Snowflake, dbt, or BigQuery.
• Strong data modeling and end-to-end domain ownership.

Responsibilities

• Own two or three investment data domains end to end.
• Ingest data from vendor sources, model it, and validate quality.
• Run pipelines, fix issues when feeds break.
• Automate routine work to reduce manual effort.
• Build analyst views and collaborate with investors on data needs.

Skills

Python

SQL

Data modeling

Databricks

Airflow

CI/CD

Tools

Snowflake

dbt

BigQuery

Job description

Seeking a mid-level Data Engineer for a well-known institutional investment office in Connecticut. They manage a large, multi-asset portfolio, and the returns fund education and research.

The team is building a modern data platform on Databricks, and it's already live. Curated data across asset classes. A data API that feeds investor-facing apps and AI analyst tools. Managers and funds reconciled across sources into one clean identity.

There's a lot left to build. You'd join a small engineering team shaping it, not maintaining something finished.

What you'll do

You'll own two or three investment data domains end to end. Think fund commitments, manager performance, cash flows, and benchmarks.

• Bring in data from vendor sources, model it, and add quality checks
• Run what you build. When a vendor feed breaks or an edge case shows up, you fix it
• Automate the routine work so each domain needs less hands-on time every quarter
• Build the views analysts use every day
• Work with investors on what each domain should answer and where it goes next

You'll report to the data team lead, get architecture direction from a senior investor-engineer, and work a step away from the CTO. Your work shows up in portfolio reviews, investment memos, and daily analytics.

What you bring

3-6 years as a data engineer on a real data platform. Hedge funds, asset managers, financial services, data-heavy tech, family offices, OCIOs, or endowments all fit.

Must-haves:

• Production-quality Python and SQL
• Real data modeling. You've designed tables and views that hold up, not just written queries against them
• Shipped on Databricks, Snowflake, dbt, or BigQuery
• Scheduling with Airflow, Prefect, Dagster, or native platform workflows
• Good CI/CD habits: pull requests, tests, deploys, monitoring
• You own what you ship. Pipeline breaks, you diagnose it. Numbers look off, you dig in

Nice-to-have

• Databricks, Delta Lake, or Unity Catalog
• Investment data: portfolio, performance, private markets, or accounting
• Data that feeds AI or LLM analyst tools

This isn't the job for you if you'd rather hand pipelines off to someone else once they ship, or you want a finished platform to plug into.