Senior Data Engineer in Toronto

Own the design, development, modernization, and operation of scalable data platforms, pipelines, and data products supporting digital experiences, investment analytics, management reporting, and enterprise decision-making. This role combines hands-on engineering with architectural judgment, technical leadership, and the development of reusable engineering patterns across cloud and hybrid environments.

Key Engineering Responsibilities

• Own end-to-end delivery of enterprise data products, spanning architecture, design, development, testing, implementation, documentation, and production support.
• Define technical architecture and design across ingestion, transformation, orchestration, storage, data modelling, serving, and observability layers.
• Build and optimize scalable batch and near-real-time data pipelines using Python, SQL, SAS, AWS Glue, Athena, PySpark, APIs, databases, file-based integrations, and cloud object storage.
• Drive modernization of legacy data ecosystems, evolving on-premises workloads toward secure, scalable cloud and lakehouse architectures while supporting hybrid integration with platforms that remain on-premises.
• Establish scalable engineering practices through reusable frameworks, development standards, automated testing, CI/CD, observability, runbooks, and operational controls.
• Make pragmatic architecture decisions that balance scalability, security, reliability, performance, cost, maintainability, and speed to market.
• Provide technical leadership across the engineering lifecycle, leading design and code reviews, resolving complex production issues, mentoring engineers, and shaping technical direction.
• Partner with business and technology stakeholders to turn complex or ambiguous requirements into scalable, production-ready data solutions.

Asset Management Data & Analytics

• Build data products that power decision-making across AUM, flows, revenue, performance, attribution, holdings, transactions, products, clients, and distribution analytics.
• Design connected data models spanning portfolios, mandates, funds, share classes, securities, issuers, benchmarks, accounts, brokers, and legal entities.
• Engineer trusted and audit-ready data through reconciliation, effective-dating, lineage, entitlements, data-quality controls, and traceability across the data lifecycle.
• Partner with investment and business SMEs to preserve critical business logic and data meaning while modernizing legacy calculations, workflows, and reporting processes.

Required qualifications

• Bachelor’s degree in computer science, engineering, data, or a related discipline, or equivalent practical experience.
• 7+ years of progressive data or software engineering experience, designing and delivering production-grade data pipelines, platforms, and analytical solutions.
• Advanced proficiency in Python and SQL, with strong engineering practices across modular development, performance optimization, automated testing, troubleshooting, and maintainable code design.
• Deep 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.
• Hands-on experience designing and delivering solutions on cloud-based data platforms, preferably AWS, spanning storage, compute, integration, security, monitoring, and deployment.
• Experience with distributed data processing frameworks such as PySpark/Spark, working across structured and semi-structured data, with a strong understanding of batch and near-real-time processing patterns.
• Strong understanding of modern engineering practices, including Git, CI/CD, automated testing, observability, monitoring, alerting, incident management, and production support.
• Proven ability to own technical design and influence engineering direction, including architecture decisions, design and code reviews, engineering standards, and technical mentorship.
• Strong communication, judgment, and prioritization skills, with the ability to navigate ambiguity, articulate technical trade-offs, and collaborate effectively across engineering, technology, and business teams.

Preferred qualifications

• Experience working with asset management, wealth management, capital markets, securities, or investment-data platforms, with an understanding of how investment data supports analytics and decision-making.
• Experience with modern data 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 on-premises or legacy platforms.
• Familiarity with enterprise data governance and management practices, including data cataloguing, lineage, data quality, master/reference data, metadata management, and access controls.
• Exposure to AI/ML and advanced analytics data patterns, including the data pipelines and platform capabilities required to support machine learning and AI-driven use cases.

What Success Looks Like

• Highly reliable and trusted data products, with clear ownership, observability, automated quality controls, and operational support embedded throughout the engineering lifecycle.
• Reusable engineering frameworks and patterns that improve consistency, reduce duplicated effort, and accelerate delivery.
• A simpler, more modern data ecosystem, reducing manual processes, operational risk, technical debt, and dependency on legacy platforms.
• Scalable, well-governed data foundations that strengthen confidence in investment, client, distribution, management, finance, and regulatory reporting.
• Stronger engineering practices across the team, through technical leadership, thoughtful architecture, effective design reviews, documentation, and mentorship.
• Faster delivery of business value by translating complex investment-data requirements into resilient, maintainable, and production-ready solutions.