Data Engineer – Global Investment Firm – 250K+
Mondrian Alpha · New York metropolitan area, PA
FULL-TIME
Posted Sep 8, 2026
Job Description
Global investment fund is hiring a data engineer to build and scale the pipelines behind its research and trading data. Heavy Databricks and Spark environment, large and messy datasets, and a direct line to the researchers and PMs who depend on the data being right.
Engineers with 3 to 10 years of real pipeline experience are encouraged to apply. This is hands-on build work, not oversight of an offshore team.
About the Role
The data engineering function sits close to the investment side. Research and trading run on the datasets this role builds: market data, alternative data, reference data, and everything that feeds signal research and analytics. The fund is consolidating this onto a modern lakehouse, so there's a lot of greenfield pipeline work alongside the job of making existing flows faster and more reliable. When a dataset is late or wrong, people notice immediately, which is part of what makes the work interesting.
What You'll Do
- Design, build, and maintain large-scale data pipelines in Databricks and Spark.
- Ingest and normalize messy, high-volume data from many sources: market data, alternative data, vendor feeds, internal systems.
- Build the lakehouse layer that research and trading teams query directly, with an eye on performance, cost, and reliability.
- Partner with quant researchers and PMs to get them clean, well-modeled, trustworthy data.
- Own data quality, monitoring, and orchestration end to end.
Must-haves:
- 3 to 10 years of data engineering experience building production pipelines.
- Strong hands-on Databricks and Spark experience. This is non-negotiable for the role.
- Solid Python and SQL, and real comfort with distributed data processing at scale.
- Experience with data modeling and building pipelines that other people actually depend on.
- Ability to reason about performance and cost, not just correctness.
Nice-to-haves:
- Experience with market data, financial data, or other high-volume time-series datasets.
- Familiarity with Delta Lake, workflow orchestration (Airflow or similar), and cloud data platforms (AWS/Azure/GCP).
- Streaming experience (Kafka, Spark Structured Streaming).
- Exposure to a quant, trading, or research environment.
Why This Role
The data here is the product for the people using it, so the work has direct, visible impact on how the fund invests. Modern stack, real scale, and the room to build pipelines properly rather than patch legacy ones forever. Compensation is top of market, and the team is small enough that a strong engineer's decisions actually shape the platform. For a data engineer who wants their pipelines to matter to the business the same day they ship, this is that.
Engineers with 3 to 10 years of real pipeline experience are encouraged to apply. This is hands-on build work, not oversight of an offshore team.
About the Role
The data engineering function sits close to the investment side. Research and trading run on the datasets this role builds: market data, alternative data, reference data, and everything that feeds signal research and analytics. The fund is consolidating this onto a modern lakehouse, so there's a lot of greenfield pipeline work alongside the job of making existing flows faster and more reliable. When a dataset is late or wrong, people notice immediately, which is part of what makes the work interesting.
What You'll Do
- Design, build, and maintain large-scale data pipelines in Databricks and Spark.
- Ingest and normalize messy, high-volume data from many sources: market data, alternative data, vendor feeds, internal systems.
- Build the lakehouse layer that research and trading teams query directly, with an eye on performance, cost, and reliability.
- Partner with quant researchers and PMs to get them clean, well-modeled, trustworthy data.
- Own data quality, monitoring, and orchestration end to end.
Must-haves:
- 3 to 10 years of data engineering experience building production pipelines.
- Strong hands-on Databricks and Spark experience. This is non-negotiable for the role.
- Solid Python and SQL, and real comfort with distributed data processing at scale.
- Experience with data modeling and building pipelines that other people actually depend on.
- Ability to reason about performance and cost, not just correctness.
Nice-to-haves:
- Experience with market data, financial data, or other high-volume time-series datasets.
- Familiarity with Delta Lake, workflow orchestration (Airflow or similar), and cloud data platforms (AWS/Azure/GCP).
- Streaming experience (Kafka, Spark Structured Streaming).
- Exposure to a quant, trading, or research environment.
Why This Role
The data here is the product for the people using it, so the work has direct, visible impact on how the fund invests. Modern stack, real scale, and the room to build pipelines properly rather than patch legacy ones forever. Compensation is top of market, and the team is small enough that a strong engineer's decisions actually shape the platform. For a data engineer who wants their pipelines to matter to the business the same day they ship, this is that.
Additional Details
- City
- New York metropolitan area
- State
- Pennsylvania
- Country
- US
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