Senior Data Engineer: Cloud Data Pipelines & Platforms

BMO; Toronto; On-site; CAD 80,000 - 175,000; Full time

Benefits offered by this job

Health insurance

Tuition reimbursement

Retirement savings plans

Job summary

BMO Financial Group is seeking an experienced data engineering leader to own the design, delivery, and operation of scalable data platforms and pipelines. You will drive modernization across cloud and hybrid environments, codify reusable patterns, and lead architectural decisions that balance performance, security, and cost.

In this role you will collaborate with business teams to turn complex requirements into production-ready data solutions supporting investment analytics, reporting, and

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 .

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.
• 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.

Skills

Python

SQL

SAS

PySpark

APIs

Data modeling

Batch processing

CI/CD

Observability

Mentoring

Communication

Education

Bachelor's degree in computer science, engineering, data, or a related discipline, or equivalent practical experience

Tools

AWS Glue

Athena

Databricks

Snowflake

Redshift

Airflow

dbt

SageMaker

Kubernetes

Job description

BMO Financial Group is seeking an experienced data engineering leader to own the design, delivery, and operation of scalable data platforms and pipelines. You will drive modernization across cloud and hybrid environments, codify reusable patterns, and lead architectural decisions that balance performance, security, and cost.

In this role you will collaborate with business teams to turn complex requirements into production-ready data solutions supporting investment analytics, reporting, and