** PLEASE NOTE THAT THIS IS AN IN OFFICE M-F APPOINTMENT. NO REMOTE, C2C OR SPONSIRSHIP PROVIDED**
What you will do:
- Build, test, and operate production data pipelines in Python on our modern pipeline framework, orchestrated with Prefect
- Build and maintain warehouse models in Snowflake using dbt clean, tested, documented, and cost-aware
- Migrate legacy pipelines and Oracle-based components onto current frameworks and standards, retiring technical debt as you go
- Work across the platform stack Kafka, Redis, InfluxDB, Oracle, and Snowflake building to the established pattern for each rather than reaching for the tool you already know
- Acquire data from external sources vendor APIs, files, feeds, and web sources and land it reliably
- Implement data quality, freshness, and reconciliation checks so problems surface before users find them
- Use AWS data services where our platform patterns call for them
- Support what you ship monitoring and alerting on your components, and investigating when something breaks
- Contribute to making platform data AI-ready, and work with the catalog and steward teams so what you build is documented, classified, and findable
- Engage directly with analysts and desk users to check that what you are building solves the actual problem
Qualifications:
- 3+ years of hands-on data engineering experience building and operating production data pipelines
- Strong Python clean, tested, maintainable code, not scripts that happen to run. Fluency with pandas and the wider data-handling ecosystem
- SQL depth you can model, query, and tune, and you know what makes a query expensive before you run it
- Snowflake or a comparable cloud warehouse dimensional modeling, performance tuning, and cost-aware design. dbt experience is a strong plus
- Breadth across data technologies streaming, caching, timeseries, relational, and warehouse, with a view on where each belongs. Kafka, Redis, InfluxDB, Oracle, and Snowflake are what we run; comparable exposure matters more than an exact match
- Pipeline orchestration experience Prefect preferred; Airflow, Dagster, or similar considered
- Sound engineering fundamentals object-oriented design, design patterns, testing, code review, and version control as habits rather than requirements
- Working knowledge of AWS data services and how to compose them into something reliable
- A build-to-operate mindset monitoring, alerting, and failure recovery are part of how you design, not something added later
- Clear communication you can explain a technical trade-off to an analyst, and turn a vague request into the right set of questions
- Terraform, Docker, or API development (FastAPI, Flask) exposure is welcome we use all three
- Experience in financial services, commodities, or energy trading data is a plus; the data volume, latency requirements, and stakes are real
