Data Engineer

Role overview

Data engineering responsibilities

• Design, develop, test, and support production data pipelines and analytical products that transform source data into curated datasets.
• Integrate data across databases, APIs, files, enterprise applications, spreadsheets, and approved external sources.
• Build scalable data models and automate recurring data preparation and transformation.
• Develop solutions using Python, SQL, SAS, AWS Glue, Athena, PySpark/Spark, APIs, and cloud data services.
• Implement validation, reconciliation, exception handling, logging, monitoring, and automated testing.
• Monitor production workloads, troubleshoot failures, and apply sustainable fixes.
• Use version control, testing, code reviews, CI/CD, documentation, and production-readiness practices.
• Partner with business, analytics, and technology teams to deliver maintainable data solutions.
• Modernize spreadsheet- and SAS-based workflows while preserving business logic and controls.

Investment data and analytics

• Develop working knowledge of investment products, including mutual funds, ETFs, pooled funds, mandates, fixed income, equities, alternatives, and cash instruments.
• Work with product, client, account, distribution, AUM, flow, revenue, fee, holdings, transaction, portfolio, and reference data.
• Build datasets connecting product, client, distribution, and investment data for business insights.
• Apply business rules and quality controls to financial data and identify inconsistencies or unclear definitions.
• Work with business and data subject-matter experts to preserve critical logic and translate needs into scalable solutions.

Required qualifications

• 5+ years in data engineering, analytics engineering, ETL development, or a related discipline, with production data solution experience.
• Bachelor’s degree in computer science, engineering, data, or a related discipline, or equivalent practical experience.
• Strong Python and SQL proficiency, including transformation, automation, validation, optimization, and troubleshooting.
• Experience with ETL/ELT, integration, orchestration, relational and dimensional modeling, warehousing, and distributed processing.
• Experience integrating databases, APIs, files, enterprise applications, and external data sources.
• Experience designing and supporting models and curated datasets for analytics, reporting, or downstream applications.
• Hands-on cloud data platform experience, preferably AWS, or strong enterprise on-premises and hybrid experience.
• Understanding of data quality, reconciliation, error handling, logging, security, monitoring, and operational controls.
• Experience with Git, automated testing, documentation, and structured software development.
• Strong communication, analytical, problem-solving, and prioritization skills; able to work across technical and business teams.

Preferred qualifications

• Experience in asset management, wealth management, banking, capital markets, distribution analytics, product analytics, or financial and management reporting.