Senior Data Engineer

Address:

Design and deliver scalable data, analytics, and automation solutions that transform complex business processes and enable new capabilities across the organization. This role combines hands-on engineering with strong analytical and business acumen to translate complex requirements into governed data models, pipelines, APIs, and intelligent automation solutions.

• Own end-to-end delivery of data and automation solutions, including pipelines, curated datasets, semantic models, APIs, and analytical data products.
• Translate complex business needs into scalable technical solutions, defining requirements, business rules, source-to-target mappings, controls, and acceptance criteria.
• Identify and engineer automation opportunities, applying conventional and AI-enabled approaches where appropriate while balancing business value, risk, controls, maintainability, and delivery effort.
• Design scalable data models for complex business and investment domains, enabling consistent definitions across reporting, analytics, digital experiences, and downstream applications.
• Build and modernize data-driven workflows using Python, SQL, SAS, AWS Glue, Athena, PySpark/Spark, APIs, and approved automation technologies.
• Engineer trust and control into data solutions through reconciliation, validation, exception management, human oversight, lineage, auditability, and data-quality controls.
• Operationalize production solutions through monitoring, documentation, support processes, failure handling, and sustainable remediation.
• Provide technical guidance and mentorship through solution design, code reviews, testing strategies, domain knowledge sharing, and production-readiness reviews.
• Communicate data lineage, solution behaviour, assumptions, dependencies, and technical trade-offs effectively across technical, business, and senior stakeholder audiences.

• Apply working knowledge of investment products and structures, including mutual funds, ETFs, pooled funds, segregated mandates, separately managed accounts, alternatives, fixed income, equities, derivatives, and cash instruments.
• Work across key investment and business data domains, including AUM/AUA, flows, revenue, holdings, transactions, performance, benchmarks, client and account hierarchies, distribution, and product/reference data.
• Design solutions around security and reference data, including identifiers, classifications, pricing, FX rates, corporate actions, and effective-dated relationships.
• Partner across business and technology teams to translate complex domain requirements into scalable, governed, and maintainable solutions.
• Build traceability and reproducibility into data products, supporting analytical, management, client, operational, and regulatory use cases.

• 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 experience in data engineering, analytics engineering, or a related discipline, with demonstrated ownership of production-grade data and automation solutions.
• Advanced proficiency in Python and SQL, with strong engineering practices across modular development, performance optimization, automated testing, troubleshooting, and maintainable code design.
• Strong experience with modern data engineering patterns, including ETL/ELT, APIs and data integration, orchestration, relational and dimensional modelling, data warehousing, lakehouse architectures, and distributed data processing.
• Proven ability to translate complex investment and operational requirements into scalable data models, controls, workflows, and technical designs.
• Experience integrating data across databases, APIs, files, vendor feeds, and enterprise applications.
• Strong experience with data profiling, mapping, reconciliation, lineage, metadata, and data-quality management.
• Hands-on experience designing and delivering solutions on cloud-based data platforms, preferably AWS, together with modern engineering practices including Git, testing, CI/CD, observability, and monitoring.
• Strong communication, facilitation, analytical, and problem-solving capabilities, with the ability to navigate ambiguity and collaborate effectively across technical and business teams.

• Experience supporting investment performance, product analytics, distribution analytics, operations, or financial and management reporting.

• Familiarity with market and reference-data providers, portfolio/accounting platforms, CRM platforms, transfer-agency systems, fund administration, or comparable investment-data ecosystems.

• Experience with modern data platforms and technologies such as AWS data services, Databricks, Snowflake, Redshift, SageMaker, Airflow, and dbt, or comparable technologies.

• Experience modernizing legacy data and reporting ecosystems, including SAS, TIBCO IBI WebFOCUS, spreadsheet-driven processes, and other legacy or on-premises platforms.

• Familiarity with enterprise data governance and control frameworks, including privacy, security, entitlements, data lineage, records management, and regulatory controls within financial services.

• Exposure to AI-enabled automation and advanced analytics patterns, including the controlled integration of AI capabilities into production workflows.

• Less reliance on manual and spreadsheet-dependent processes through scalable, controlled, and supportable automation.
• Consistent business definitions and reusable investment-data models across analytical and operational data products.
• Faster translation of business needs into production solutions, connecting investment-domain requirements with technically sound designs.
• Fewer reporting breaks, manual interventions, and downstream data issues through stronger controls, reconciliation, exception handling, and observability.
• Greater stakeholder confidence in critical investment data, including AUM, flows, revenue, performance, product, client, and operational information.
• Practical adoption of automation and AI capabilities where they deliver measurable value while maintaining appropriate controls, traceability, and human oversight.
• Stronger collaboration between business and operations teams, with complex investment concepts translated into maintainable data models, workflows, and engineering solutions.