Python Developer for asset manager

• As an Engineer, Investment Technology, you will be working on the application team that builds and supports Separately managed accounts platform.
• The Fixed Income SMA team relies on high-quality data pipelines and internal tooling to support portfolio management, trading, and research workflows. We are adding a Senior Data Engineer to help build and maintain the data layer behind these systems, with a particular focus on reliable ingestion, data modeling, analytical datasets, and practical internal applications.
• This role sits within Investment Technology but will work closely with the SMA quantitative research and engineering team. This role is a chance to help shape the data foundation for a fast-growing front-office platform while working closely with a highly technical investment-facing team. The work is practical, visible, and directly connected to trading, research, and portfolio outcomes.

Responsibilities of the Role:

• Build and maintain data pipelines ingesting from internal systems, market data vendors, and operational databases into analytical and application-facing stores
• Design and manage PostgreSQL and analytical data models that support front-office workflows and quantitative research
• Develop production Python and SQL code for ETL, validation, enrichment, and data quality monitoring
• Build and maintain lightweight internal applications and reporting interfaces, including browser-based tools using JavaScript where helpful
• Support research and analytics workflows involving large financial and time series datasets
• Partner with quantitative developers and investment users to improve data usability, transparency, and operational reliability

Requirements of the Role:

• 4+ years of experience in data engineering, analytics engineering, or software engineering with a strong data focus
• Strong Python experience required.
• Experience with relational databases; PostgresSql / Snowflake experience is a plus.
• Experience designing and operating ETL pipelines and data models in production
• Familiarity with dbt, AWS, Docker, Kubernetes (EKS), Kafka, Redis, and cloud data infrastructure is preferred
• Experience working with financial, transactional, or time series data is strongly preferred
• Some JavaScript experience for internal tools, dashboards, or data-grid-based interfaces is preferred