Data Engineer

Location: Toronto

Final date to receive applications: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 qualifications5+ 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…