Address:

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.

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards

About Us

At BMO we are driven by a shared Purpose: Boldly Grow the Good in business and life. It calls on us to create lasting, positive change for our customers, our communities and our people. By working together, innovating and pushing boundaries, we transform lives and businesses, and power economic growth around the world.

As a member of the BMO team you are valued, respected and heard, and you have more ways to grow and make an impact. We strive to help you make an impact from day one – for yourself and our customers. We’ll support you with the tools and resources you need to reach new milestones, as you help our customers reach theirs. From in-depth training and coaching, to manager support and network-building opportunities, we’ll help you gain valuable experience, and broaden your skillset.

To find out more visit us at https://jobs.bmo.com/ca/en.

BMO is committed to an inclusive, equitable and accessible workplace. By learning from each other’s differences, we gain strength through our people and our perspectives. Accommodations are available on request for candidates taking part in all aspects of the selection process. To request accommodation, please contact your recruiter.

Note to Recruiters: BMO does not accept unsolicited resumes from any source other than directly from a candidate. Any unsolicited resumes sent to BMO, directly or indirectly, will be considered BMO property. BMO will not pay a fee for any placement resulting from the receipt of an unsolicited resume. A recruiting agency must first have a valid, written and fully executed agency agreement contract for service to submit resumes.