Senior Data Engineer

Application Deadline:

11/06/2026

Address:

100 King Street West

Job Family Group:

Data Analytics & Reporting

Design and deliver scalable data, analytics, and automation solutions that transform complex business processes and enable new capabilities across the organization. This role combines hands-on engineering with strong analytical and business acumen to translate complex requirements into governed data models, pipelines, APIs, and intelligent automation solutions.

Key Engineering & Automation Responsibilities

• •Own end-to-end delivery of data and automation solutions , including pipelines, curated datasets, semantic models, APIs, and analytical data products.
• •Translate complex business needs into scalable technical solutions , defining requirements, business rules, source-to-target mappings, controls, and acceptance criteria.
• •Identify and engineer automation opportunities , applying conventional and AI-enabled approaches where appropriate while balancing business value, risk, controls, maintainability, and delivery effort.
• •Design scalable data models for complex business and investment domains , enabling consistent definitions across reporting, analytics, digital experiences, and downstream applications.
• •Build and modernize data-driven workflows using Python, SQL, SAS, AWS Glue, Athena, PySpark/Spark, APIs, and approved automation technologies.
• •Engineer trust and control into data solutions through reconciliation, validation, exception management, human oversight, lineage, auditability, and data-quality controls.
• •Operationalize production solutions through monitoring, documentation, support processes, failure handling, and sustainable remediation.
• •Provide technical guidance and mentorship through solution design, code reviews, testing strategies, domain knowledge sharing, and production-readiness reviews.
• •Communicate data lineage, solution behaviour, assumptions, dependencies, and technical trade-offs effectively across technical, business, and senior stakeholder audiences.

Investment Data & Asset Management

• •Apply working knowledge of investment products and structures , including mutual funds, ETFs, pooled funds, segregated mandates, separately managed accounts, alternatives, fixed income, equities, derivatives, and cash instruments.
• •Work across key investment and business data domains , including AUM/AUA, flows, revenue, holdings, transactions, performance, benchmarks, client and account hierarchies, distribution, and product/reference data.
• •Design solutions around security and reference data , including identifiers, classifications, pricing, FX rates, corporate actions, and effective-dated relationships.
• •Partner across business and technology teams to translate complex domain requirements into scalable, governed, and maintainable solutions.
• •Build traceability and reproducibility into data products , supporting analytical, management, client, operational, and regulatory use cases.

Required qualifications

• •Bachelor’s degree in computer science, engineering, data, or a related discipline, or equivalent practical experience; investment-industry education or credentials are an asset.
• •7+ years of experience in data engineering, analytics engineering, or a related discipline , with demonstrated ownership of production-grade data and automation solutions.
• •Advanced proficiency in Python and SQL , with strong engineering practices across modular development, performance optimization, automated testing, troubleshooting, and maintainable code design.
• •Strong 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.
• •Proven ability to translate complex investment and operational requirements into scalable data models, controls, workflows, and technical designs .
• •Experience integrating data across databases, APIs, files, vendor feeds, and enterprise applications .
• •Strong experience with data profiling, mapping, reconciliation, lineage, metadata, and data-quality management .
• •Hands-on experience designing and delivering solutions on cloud-based data platforms , preferably AWS, together with modern engineering practices including Git, testing, CI/CD, observability, and monitoring.
• •Strong communication, facilitation, analytical, and problem-solving capabilities, with the ability to navigate ambiguity and collaborate effectively across technical and business teams .

Preferred qualifications

Experience supporting investment performance, product analytics, distribution analytics, operations, or financial and management reporting .

Familiarity with market and reference-data providers, portfolio/accounting platforms, CRM platforms, transfer-agency systems, fund administration, or comparable investment-data ecosystems .

Experience with modern data platforms and 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 legacy or on-premises platforms.

Familiarity with enterprise data governance and control frameworks , including privacy, security, entitlements, data lineage, records management, and regulatory controls within financial services.

Exposure to AI-enabled automation and advanced analytics patterns , including the controlled integration of AI capabilities into production workflows.

What Success Looks Like

• •Less reliance on manual and spreadsheet-dependent processes through scalable, controlled, and supportable automation.
• •Consistent business definitions and reusable investment-data models across analytical and operational data products.
• •Faster translation of business needs into production solutions , connecting investment-domain requirements with technically sound designs.
• •Fewer reporting breaks, manual interventions, and downstream data issues through stronger controls, reconciliation, exception handling, and observability.
• •Greater stakeholder confidence in critical investment data , including AUM, flows, revenue, performance, product, client, and operational information.
• •Practical adoption of automation and AI capabilities where they deliver measurable value while maintaining appropriate controls, traceability, and human oversight.
• •Stronger collaboration between business and operations teams , with complex investment concepts translated into maintainable data models, workflows, and engineering solutions.

Salary :

$80,000.00 - $175,000.00

Pay Type:

Salaried

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