Data Engineer

ScotiabankToronto, ON
Onsite

About The Position

The Data Engineer plays a critical role within the Enterprise Data & AI Technology organization—one of Scotiabank’s most significant enterprise-wide strategic initiatives. This organization drives data enabled decision making, AI innovation, and technology modernization across the Bank.

Requirements

  • Experience building data pipelines, and composable cloud-based data platforms (Azure), using Azure Data Factory, ADLS.
  • Experience working with Lakehouse platforms: Databricks, Unity Catalog, Auto Loader
  • Experience with data engineering, programming, ETL and ELT processes for data extraction and processing
  • Experience working with structured, semi-structured, and unstructured data.
  • Strong programming and scripting skills (SQL, Python, Java, Scala)
  • Strong knowledge of cloud infrastructure and microservices architecture
  • Experience in creating technical design documentations, both logical and physical view
  • Experience collaborating and working with DevOps, Scrum, and Product Teams
  • Demonstrated team player with strong communication skills and a track record of successful delivery of product development.
  • Solid communication skills – ability to translate complex technical problems to business language
  • Experience in leading and driving technical discussions
  • Expert at problem solving and debugging
  • 4+ years of experience working with Data Warehouse / Data Platforms
  • 4+ years of experience creating ELT / ETL data pipelines, working with structured, semi-structured, and unstructured data
  • 4+ years of experience working on continuous integrations and continuous deployment pipeline (CI/CD) and working with source control systems such as Github, Bitbucket, and Terraform
  • Must have 4+ years of experience in Cloud: Azure
  • Must have 4+ years of experience in Lakehouse: Databricks

Responsibilities

  • Design, build, and maintain scalable, resilient data pipelines to support the ingestion, processing, and distribution of large datasets.
  • Leverage SaaS platforms and tools to develop, configure, and automate data workflows, improving efficiency across the data engineering lifecycle.
  • Partner with stakeholders and product managers to gather requirements, design ingestion patterns, and onboard new data sources into the cloud data platform.
  • Implement observability capabilities and continuously optimize application services and pipeline performance.
  • Perform data integrity validation to ensure accuracy, reliability, and consistency of data.

Benefits

  • Upskilling through online courses, cross-functional development opportunities, and tuition assistance.
  • Competitive Rewards program including bonus, flexible vacation, personal, sick days and benefits will start on day one.
  • Free tea & coffee, universal washrooms, and lots of space for team collaboration.
  • Opportunities for community engagement & belonging with our various programs.
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