Data Engineer

Application Deadline: 11/06/2026 Address: 100 King Street West Job Family Group: Data Analytics & Reporting Design, build, and support scalable data pipelines, models, and analytical data products that enable deeper business insights and data-driven decision-making. This role combines hands-on data engineering with business and analytical acumen to integrate complex data sources, automate data preparation, and deliver trusted, well-governed datasets across cloud and hybrid environments. Key Engineering Responsibilities • Design, develop, test, and support production-grade data pipelines and analytical data products, transforming data from multiple sources into curated, business-ready datasets. • Integrate and transform data across databases, APIs, files, enterprise applications, spreadsheets, and approved external data sources. • Build scalable data models that enable consistent business definitions and support analytics, reporting, and downstream applications. • Automate recurring data preparation and transformation processes, reducing manual effort and improving the timeliness, consistency, and reliability of data delivery. • Develop data solutions using Python, SQL, SAS, AWS Glue, Athena, PySpark/Spark, APIs, and cloud-based data services, applying established engineering patterns and standards. • Embed data-quality and operational controls through validation, reconciliation, exception handling, logging, monitoring, and automated testing. • Support production data products, monitoring scheduled workloads, troubleshooting failures and data issues, and implementing sustainable solutions. • Apply modern software-engineering practices, including version control, unit and integration testing, code reviews, CI/CD, documentation, and production-readiness standards. • Partner with business, analytics, and technology teams to translate requirements into maintainable data models, pipelines, and analytical solutions. • Support modernization of legacy data and reporting processes, including spreadsheet- and SAS-based workflows, while preserving required business logic and controls. Investment Data & Analytics • Develop working knowledge of investment products and structures, including mutual funds, ETFs, pooled funds, segregated mandates, separately managed accounts, fixed income, equities, alternatives, and cash instruments. • Work across investment and business data domains, including products, clients, accounts, distribution channels, AUM, flows, revenue and fees, holdings, transactions, portfolios, and reference data. • Build analytical datasets and models that connect product, client, distribution, and investment data to support meaningful business insights. • Apply business rules and data-quality controls to financial and investment data, identifying inconsistencies and escalating unclear definitions or control gaps. • Partner with business and data SMEs to understand data meaning, preserve critical business logic, and translate analytical requirements into scalable data solutions. Required qualifications • 5+ years of experience in data engineering, analytics engineering, ETL development, or a related discipline, with experience building and supporting production data solutions. • Bachelor’s degree in computer science, engineering, data, or a related discipline, or equivalent practical experience. • Strong proficiency in Python and SQL, with hands-on experience in data transformation, automation, validation, performance optimization, and troubleshooting. • Experience with modern data engineering patterns, including ETL/ELT, data integration, orchestration, relational and dimensional modelling, data warehousing, and distributed data processing. • Experience integrating data from databases, APIs, files, enterprise applications, and external data sources. • Experience designing and supporting data models and curated datasets used for analytics, reporting, or downstream applications. • Hands-on experience with cloud-based data platforms, preferably AWS, or strong experience across enterprise on-premises and hybrid data environments. • Understanding of data quality, reconciliation, error handling, logging, security, monitoring, and operational controls. • Experience with Git, automated testing, documentation, and structured software-development practices. • Strong communication, analytical, problem-solving, and prioritization skills, with the ability to work effectively 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. • Working knowledge of investment products and business measures, with the ability to translate analytical questions into reliable datasets and data models. • Experience with modern data platforms and technologies such as AWS data services, Databricks, Snowflake, Redshift, AWS Glue, Airflow, dbt, Spark/PySpark, Power BI, or comparable technologies. • Experience modernizing legacy data and reporting solutions, including SAS, TIBCO IBI WebFOCUS, spreadsheet-driven processes, or other legacy platforms. • Familiarity with CI/CD, APIs, automated testing, data migration, and cloud modernization practices. • Exposure to AI/ML or advanced analytics workflows and the data engineering patterns that enable them. What Success Looks Like • Reliable, well-tested, and maintainable data products that deliver complete, timely, and trusted data for analytics and decision-making. • Reduced manual data preparation and processing through practical automation and reusable engineering components. • Consistent and reusable data models that improve how business, client, product, distribution, and investment data is consumed across analytical solutions. • Fewer data-quality issues and reporting breaks through effective validation, reconciliation, monitoring, and exception handling. • Strong operational readiness through clear documentation, lineage, monitoring, runbooks, and support practices. • Growing investment-domain expertise, connecting technical implementation with business context and asset-management outcomes. Salary $65,000.00 - $140,000.00 Pay Type: Salaried