Data Engineer I

TD•Toronto, ON

1d•CA$69,700 - CA$98,400•Onsite

About The Position

We are the CPB Data team. We own the pipelines that bring data into our platform and the curated data products that flow back out to our partners. From onboarding a brand-new source, to building and hardening the ETL that moves and shapes it, to publishing governed data products, we are the team that turns raw, scattered data into trusted, reusable assets the wider business depends on. You will join a close-knit group of data engineers and analysts who take real ownership of what they build. We value people who dig into problems end to end, recognize each other's contributions, and are always looking for a smarter, faster way to work — including making the most of modern AI tooling. Everything downstream — analytics, reporting, AI models, regulatory and compliance deliverables — depends on data arriving cleanly, securely, and on time. As a Data Engineer on this team, you are at the front of that chain. You will onboard new sources into our ingestion framework, build and debug the Databricks notebooks and ETL that process them, and open the right paths through firewalls and access controls so the data can flow. Your work directly determines how quickly the business can act on new data and how much they can trust it.

Requirements

Hands-on experience building and debugging data pipelines on Azure Databricks, Azure Data Factory, ADLS, and Delta Lake.
Strong programming skills in Python, plus practical experience with Spark and PySpark for large-scale data processing.
Solid grounding in relational databases and SQL, with the ability to provision and troubleshoot access across a variety of database technologies.
Working knowledge of networking fundamentals — enough to open firewalls to data sources and reason about connectivity between systems.
Comfort with API basics and common authentication patterns, and the judgment to write secure, high-performance ETL code.

Nice To Haves

Experience onboarding new sources into an established ingestion framework.
Familiarity with building or exposing curated data products for downstream or partner consumption.
Exposure to data governance, data quality, or data lifecycle management practices.
Experience using AI-assisted development tools to speed up engineering work.
A track record of strong end-to-end ownership and mentoring or recognizing teammates.

Responsibilities

Independently design, build, and debug Databricks notebooks and ETL pipelines that ingest and transform data from a wide range of sources.
Onboard new data sources into our ingestion framework end to end — from first connection through to a production-ready, monitored pipeline.
Establish secure connectivity to source systems: work through networking fundamentals to open firewalls, and provision the right access across diverse database and API technologies.
Write secure, high-performance ETL code that scales, using Python, Spark, and PySpark on Azure Data Factory, Azure Databricks, ADLS, and Delta Lake.
Own the reliability of your pipelines — troubleshoot issues, tune performance, and keep data flowing accurately into curated data products.
Collaborate with source owners, platform, networking, and partner teams to deliver ingestion and data-product work on the release cadence.