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Senior Data Engineer

BMO Financial Group
Toronto, CAN
Full-time
Onsite
Discovered 5 days ago
PythonSQLData pipelines and ETL/ELTAWS data platformsPySpark/SparkData modelling

Full Job Posting

Role overview

Own the design, development, modernization, and operation of scalable data platforms, pipelines, and data products.

Support digital experiences, investment analytics, management reporting, and enterprise decision-making.

The role combines hands-on engineering, architectural judgment, technical leadership, and reusable engineering patterns across cloud and hybrid environments.

Engineering responsibilities

  • Own enterprise data product delivery from architecture and design through development, testing, implementation, documentation, and production support.
  • Define architecture for ingestion, transformation, orchestration, storage, data modelling, serving, and observability.
  • Build and optimize batch and near-real-time pipelines using Python, SQL, SAS, AWS Glue, Athena, PySpark, APIs, databases, file integrations, and cloud object storage.
  • Modernize on-premises data workloads toward secure cloud and lakehouse architectures while supporting hybrid integration.
  • Establish reusable frameworks, standards, automated testing, CI/CD, observability, runbooks, and operational controls.
  • Balance scalability, security, reliability, performance, cost, maintainability, and speed to market in architecture decisions.
  • Lead reviews, resolve production issues, mentor engineers, and shape technical direction.
  • Partner with business and technology stakeholders to deliver scalable production-ready solutions.

Asset management data and analytics

  • Build products supporting decisions 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.
  • Provide trusted, audit-ready data through reconciliation, effective dating, lineage, entitlements, data-quality controls, and traceability.
  • Partner with investment and business experts to preserve business logic while modernizing legacy calculations, workflows, and reporting.

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 delivering production-grade data pipelines, platforms, and analytical solutions.
  • Advanced Python and SQL proficiency, including modular development, performance optimization, testing, troubleshooting, and maintainable code.
  • Deep experience with ETL/ELT, APIs, integration, orchestration, data modelling, warehousing, lakehouse architectures, and distributed processing.
  • Hands-on experience delivering cloud-based data platform solutions; AWS is preferred.
  • Experience with PySpark/Spark and structured and semi-structured batch and near-real-time processing.
  • Knowledge of Git, CI/CD, testing, observability, monitoring, alerting, incident management, and production support.
  • Ability to own technical design, influence engineering direction, mentor engineers, communicate trade-offs, and collaborate across teams.

Preferred qualifications

  • Experience with asset management, wealth management, capital markets, securities, or investment-data platforms.
  • Experience with modern data technologies such as AWS data services, Databricks, Snowflake, Redshift, SageMaker, Airflow, or dbt.

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