Jr. Data Engineer at BMO

Toronto, Ontario, Canada

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Jr. Data Engineer role at BMO in Toronto, Ontario, Canada, focusing on building robust data pipelines and collaborating with data scientists and analytics teams to support ML operations and business insights. The position involves transforming both structured and unstructured data, ensuring data quality and governance, and creating automated processes that empower stakeholders with reliable metrics and insights. The role supports business transformation initiatives, requires strong technical and communication skills, and emphasizes clean, maintainable code and production readiness.

Salary

CAD 67,200 - 124,200/year

Requirements

Education

  • post-secondary degree in a related field of study or equivalent combination of education and experience

Experience

  • 4+ years production development experience
  • 4+ years relevant work experience as a Data Operations Engineer
  • 4+ years hands‑on experience building production ETL/ELT solutions for large‑scale data pipelines

Skills

  • consultative approach to engaging internal partners
  • creative reasoning and computational thinking
  • clear verbal and written communication
  • extensive collaboration and teamwork
  • experience impacting business transformation with data
  • proven experience building data pipelines in production for advanced analytics use cases
  • practical knowledge of DevOps, DataOps, and MLOps
  • proficiency in writing clean, maintainable, scalable, and production‑ready code
  • experience utilizing NLP to transform unstructured data
  • strong proficiency with SQL across various databases
  • familiarity with distributed computing frameworks such as Spark and Dask
  • familiarity with analytics libraries such as pandas, NumPy, and matplotlib
  • familiarity with generative AI use, optimization, and tools (APIs, Vector DBs, Prompt Engineering)
  • familiarity with cloud platforms (AWS, Azure, GCP)

Languages

  • English

Responsibilities

  • Consult with data scientists, data and analytics teams, technology partners, and business partners to create pipelines, governance, and solutions that impact model ops, data, and insight challenges
  • Wrap model ops and analytics in clean, repeatable, and automated processes
  • Elevate business stakeholders’ confidence in data
  • Become the expert on the data sourced and used in ML operations
  • Transform structured and unstructured data for ML and analytics programs
  • Ensure production behavioral scores and business metrics align with expectations
  • Identify and explain deviations in metrics proactively and initiate resolution paths
  • Diagnose and solve problems within given rules
  • Clarify key business metrics and implement them at scale
  • Prepare modeling master data sets in collaboration with data scientists
  • Create insights and visualizations that answer high‑impact business questions
  • Prepare model validation documentation

Technologies

SparkDaskpandasNumPymatplotlibAWSAzureGCPNLPVector databasesPrompt Engineering APIsSQLDevOpsDataOpsMLOps

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