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Job Description & How to Apply Below
Work Location:
Toronto, Ontario, Canada
Hours:
37.5
Line of Business:
Technology Solutions Pay Details:$69,700 - $98,400 CADTD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.
As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.
Job Description:
Purpose We are the Data Strategy & Implementation team. We own the pipelines thatbring data into our platform and the curated data products that flow back outto 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 arethe team that turns raw, scattered data into trusted, reusable assets the wider business depends on.
People 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.
Presence We work in a hybrid model, combining focused remote days with regular in-person collaboration. You will partner daily with source-system owners, platform and networking teams, and the partners who consume our data products.
WHY THIS ROLE MATTERS Everything downstream — analytics, reporting, AI models, regulatory and compliance deliverables — depends on data arriving cleanly, securely, and ontime. As a Data Engineer on this team, you are at the front of that chain. Youwill 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 cantrust it.
WHAT YOU'LL DO Independently design, build, and debug Databricks notebooks and ETLpipelines 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, andPySpark 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 todeliver ingestion and data-product work on the release cadence.
WHERE YOU'LL WORKThis is a hybrid role. You will spend part of your week working remotely andpart collaborating with the team and stakeholders in person. Expect closeday-to-day partnership with the data engineers, analysts, and platform teams whokeep our ingestion and data-product delivery moving.
EXPERIENCE AND / OR EDUCATION 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 andPySpark 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 todata sources and reason about connectivity between systems.
Comfort with API basics and common authentication patterns, and the judgment to write…
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