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Data Engineering Intern
Pipelines, models, and warehouses. Turning the four spreadsheets and one legacy database a client actually has into something they can ask questions of.
Apply for this role- Location
- Remote, with Regina, SK as home base
- Commitment
- Part-time during term, full-time over the summer
- Intake
- Summer 2027 intake, rolling review
- Team
- Data & Analytics
- Level
- Student or recent graduate
About the role
Clients rarely arrive with clean data. They arrive with an export, a Google Sheet somebody maintains by hand, and a database whose schema has been argued about for six years.
Your job is to get that into a shape where a question can be answered the same way twice. That means ingestion, modelling, tests, and documentation, in roughly that order of glamour and reverse order of importance.
What you will actually do
- Build ingestion jobs from APIs, files, and databases, and make them re-runnable.
- Model data into tables analysts and dashboards can use without a phone call.
- Write data quality tests, so a silently broken pipeline stops being silent.
- Document what a column means and where it came from.
- Tune queries that got slow as the data grew.
What you need on day one
- SQL you can write without a reference open: joins, aggregates, window functions if you have met them.
- Python for scripting and transformation.
- Care about correctness. A number on a dashboard is a claim, and you should want to be able to defend it.
Nice to have, genuinely optional
None of these are requirements. They are listed so you know what would be a head start, not so you can talk yourself out of applying. Apply if the list above fits.
- dbt, Airflow, Dagster, or any orchestration tool.
- Warehouse experience: BigQuery, Snowflake, Redshift, or plain Postgres at scale.
- Pandas or Polars.
- Dimensional modelling, or having read anything by Kimball.
What you should expect to learn
- How to design a schema that answers questions instead of storing rows.
- How to make a pipeline trustworthy, which is mostly about what happens when it fails.
- How to translate a vague business question into a query.