Data Engineer (Contract)
About the Role
My client are looking for an experienced Data Engineer to join their team in Boston on an initial 12-month contract basis. This is a hands-on, delivery-focused role designed to carry a critical body of work through to completion. The successful candidate will be working close to the data every day: diagnosing issues, building and optimising pipelines, and delivering incremental progress within an existing codebase and data landscape.
This is not a strategy or architecture role. My client needs an executor: someone technically strong, comfortable in the detail, and capable of making consistent forward progress with limited oversight.
Key Responsibilities
- Write, optimise, and performance-tune complex SQL queries against large financial datasets
- Develop and maintain Python-based data pipelines and processing workflows
- Work with PostgreSQL, Parquet, and modern data engineering patterns on a daily basis
- Diagnose and resolve data quality issues, ensuring pipeline resiliency and reliability
- Apply orchestration best practices, including job partitioning to prevent full reruns on pipeline failures
- Handle financial and investment data including portfolios, reference data, positions, and analytics inputs
- Track tasks, manage your own workload, and keep progress moving in ambiguous or evolving situations
- Collaborate with internal stakeholders and operate independently within existing systems and documentation
Required Experience
- Strong, demonstrable SQL skills including performance tuning against large datasets, across PostgreSQL and/or SQL Server
- Solid Python experience (3+ years) for data processing and pipeline development
- Hands-on experience with PostgreSQL, Parquet, and medallion architecture
- Familiarity with Apache Spark and distributed data processing
- Exposure to Snowflake, including features such as time travel and partitioning strategies
- Proven experience working with financial or investment data: portfolios, reference data, positions, or analytics inputs
- Ability to operate independently within an existing codebase and data environment
- Data quality tooling experience, for example Great Expectations or equivalent
Highly Desirable
- FactSet experience, particularly across multiple modules including Portfolio Analytics (PA)
- Experience handling delayed or late-arriving data in financial or insurance workflows
- Familiarity with IBOR data and related import/partitioning patterns
- Comfort working in-office in a collaborative team environment
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