Associate, Data Engineer

BMO•Toronto, ON

2d•CA$90,000 - CA$120,000•Onsite

About The Position

We are seeking a skilled and motivated Data Engineer to join our Data Cognition Team at BMO Capital Markets. In this role, you will design, build, and maintain robust data infrastructure and scalable platforms that enable advanced analytics and machine learning across Investment Banking and Global Markets. You will collaborate with AI engineers and data scientists to deliver high-quality, data-centric solutions that empower business decisions. The Data Cognition Team (DCT) at BMO Capital Markets delivers a sustainable and scalable suite of AI-enabled data products and platforms for multiple business units. We leverage the latest data engineering and analytics technologies to solve complex business challenges and drive strategic transformation across Investment Banking, Global Markets, and other divisions.

Requirements

Bachelor’s or Graduate degree in Engineering, Computer Science, Mathematics, Physics, or related quantitative discipline.
Strong programming skills in Python, Scala, or Java, with a passion for learning new technologies.
Expertise in distributed computing, stream processing, and application development using Spark and Kafka.
Proficiency in data modeling, database design, and ETL processes.
Experience deploying data solutions in cloud environments (AWS, Azure, GCP).
Familiarity with containerization and orchestration tools (Docker, Kubernetes).
Excellent problem-solving and analytical skills with attention to detail.
Strong communication skills for collaboration with technical and business stakeholders.

Nice To Haves

Financial domain knowledge: familiarity with investment banking concepts, trading strategies, and financial data formats (e.g., time series, tick data).
Experience with Generative AI or Agentic AI technologies, including integration into data-driven solutions.

Responsibilities

Design, develop, and optimize scalable data pipelines for ingesting, processing, and storing large volumes of structured and unstructured data.
Implement distributed data processing solutions using technologies such as Spark, Kafka, and cloud-native services.
Ensure data integrity, reliability, and high availability in mission-critical workloads.
Model and design databases and data architectures to support advanced analytics, machine learning, and AI applications.
Develop and maintain ETL processes for transforming and loading data from diverse sources.
Deploy data infrastructure and distributed computing environments in the cloud using containerization (Docker, Kubernetes).
Monitor, benchmark, and tune data processing applications for optimal performance and scalability.
Collaborate with cross-functional stakeholders to understand business requirements and deliver tailored data solutions.
Implement robust testing, observability, and monitoring solutions to maintain system health and performance.
Stay current with technology trends, best practices, and industry standards in data engineering and platform development.