Senior Data Engineer

Role overview

Engineering and automation

• Own end-to-end delivery of pipelines, curated datasets, semantic models, APIs and analytical data products.
• Translate business needs into technical requirements, rules, mappings, controls and acceptance criteria.
• Identify automation opportunities and balance business value, risk, controls, maintainability and delivery effort.
• Design scalable business and investment data models for consistent reporting, analytics and downstream use.
• Build workflows using Python, SQL, SAS, AWS Glue, Athena, PySpark/Spark, APIs and approved automation tools.
• Engineer reconciliation, validation, exception management, human oversight, lineage, auditability and data-quality controls.
• Operationalize production solutions with monitoring, documentation, support, failure handling and remediation.
• Provide technical guidance through solution design, code reviews, testing and production-readiness reviews.
• Communicate lineage, behavior, assumptions, dependencies and trade-offs to technical, business and senior stakeholders.

Investment data and collaboration

• Apply working knowledge of investment products and structures, including funds, mandates, alternatives, fixed income, equities, derivatives and cash instruments.
• Work across data domains including assets, flows, revenue, holdings, transactions, performance, benchmarks, client and account hierarchies, distribution and product/reference data.
• Design security and reference data solutions covering identifiers, classifications, pricing, FX rates, corporate actions and effective-dated relationships.
• Partner across business and technology teams to deliver scalable, governed and maintainable solutions.
• Build traceability and reproducibility for analytical, management, client, operational and regulatory uses.

Required qualifications

• Bachelor’s degree in computer science, engineering, data or a related discipline, or equivalent practical experience; investment-industry education or credentials are an asset.
• 7+ years of data engineering, analytics engineering or related experience, including ownership of production-grade data and automation solutions.
• Advanced Python and SQL proficiency, including modular development, optimization, testing, troubleshooting and maintainable code.
• Strong experience with ETL/ELT, APIs, integration, orchestration, data modeling, warehousing, lakehouse architectures and distributed processing.
• Ability to translate complex investment and operational requirements into scalable models, controls, workflows and technical designs.
• Experience integrating data from databases, APIs, files, vendor feeds and enterprise applications.
• Strong data profiling, mapping, reconciliation, lineage, metadata and data-quality management experience.
• Hands-on cloud data-platform delivery, preferably AWS; modern practices include Git, testing, CI/CD, observability and monitoring.
• Strong communication, facilitation, analytical and problem-solving skills; able to navigate ambiguity and collaborate across teams.