Data Engineer
Gambit Technologies · New Haven, Connecticut, United States
FULL TIMEpermanent
Job Description
Data Engineer Hybrid New Haven, CT
To be considered for an interview, please make sure your application is full in line with the job specs as found below.
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 doYou'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 checksRun what you build. When a vendor feed breaks or an edge case shows up, you fix itAutomate the routine work so each domain needs less hands-on time every quarterBuild the views analysts use every dayWork with investors on what each domain should answer and where it goes nextYou'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 bring3-6 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 SQLReal data modeling. You've designed tables and views that hold up, not just written queries against themShipped on Databricks, Snowflake, dbt, or BigQueryScheduling with Airflow, Prefect, Dagster, or native platform workflowsGood CI/CD habits: pull requests, tests, deploys, monitoringYou own what you ship. xzbcwsv Pipeline breaks, you diagnose it. Numbers look off, you dig in
Nice-to-haveDatabricks, Delta Lake, or Unity CatalogInvestment data: portfolio, performance, private markets, or accountingData 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.
To be considered for an interview, please make sure your application is full in line with the job specs as found below.
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 doYou'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 checksRun what you build. When a vendor feed breaks or an edge case shows up, you fix itAutomate the routine work so each domain needs less hands-on time every quarterBuild the views analysts use every dayWork with investors on what each domain should answer and where it goes nextYou'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 bring3-6 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 SQLReal data modeling. You've designed tables and views that hold up, not just written queries against themShipped on Databricks, Snowflake, dbt, or BigQueryScheduling with Airflow, Prefect, Dagster, or native platform workflowsGood CI/CD habits: pull requests, tests, deploys, monitoringYou own what you ship. xzbcwsv Pipeline breaks, you diagnose it. Numbers look off, you dig in
Nice-to-haveDatabricks, Delta Lake, or Unity CatalogInvestment data: portfolio, performance, private markets, or accountingData 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.
Details
| Company | Gambit Technologies |
| Location | New Haven, Connecticut, United States |
| Type | FULL TIME |
| Niche | finance |
| Experience | permanent |