Data Engineer

Responsibilities

Design, build, and maintain robust

ETL/ELT pipelines

for large financial datasets.

Develop high-performance data processing workflows using

Python and Pandas

.

Build and optimize pipelines using

distributed computing frameworks

where required.

Integrate data from APIs, databases, files, and external data vendors.

Design processes for data validation, reconciliation, monitoring, and error handling.

Improve the performance, scalability, and reliability of existing data infrastructure.

Build reusable data libraries and services consumed by research and application teams.

Work with large historical and time-series datasets.

Diagnose production data issues and improve observability of data pipelines.

Collaborate directly with quantitative researchers and software engineers to translate data requirements into production systems.

Minimum Requirements

Strong programming skills in

Python

.

Strong working knowledge of

Pandas

and numerical/data-processing workflows.

Experience designing and maintaining

ETL/ELT pipelines

.

Experience with at least one

distributed computing framework

such as Dask, Spark, Ray, or similar.

Strong understanding of databases, data structures, and efficient data processing.

Comfortable working with large datasets and debugging complex data-quality issues.

Strong problem-solving skills and ability to work independently.

Good to Have

Experience with

AWS

or another major cloud platform.

Experience with

SQL, PostgreSQL, Parquet, S3

, or similar technologies.

Experience optimizing Python workloads for memory and compute efficiency.

Familiarity with orchestration tools such as Airflow or Prefect.

Experience with financial, market, or time-series data.

Familiarity with Docker and production deployment environments.

Knowledge of financial markets is helpful but

not required

.

What You'll Get

You will join a small, highly technical team where engineers have substantial ownership over the systems they build. Rather than maintaining a narrow component of a large organization, you will have the opportunity to design core infrastructure from the ground up and see it used directly in institutional investment workflows.

We value strong engineering fundamentals, intellectual curiosity, and people who enjoy solving difficult data problems.