Free learning pathL0–L4

Data engineering

Build the foundations, methods and work samples used in data engineering roles.

An entry-level learning direction with educational tasks; check target-role requirements.

Practice setWork-sample templateJobs, resources and experiencesJobs, resources and experiences

Your learning checklist

Required items: 0 / 10 completed or already known

Device storage

Next: Read a role through its responsibilities

Checklist statuses are self-reported; exercise scores are recorded separately. You can reset any item.

L0

Know the role

  1. Read a role through its responsibilitiesRequired

    Translate a role description into three observable learning goals.

L1

Build foundations

  1. Spreadsheet formulas and reconciliationRequired

    Build a sourced calculation table with an independent check.

  2. Statistics, samples and uncertaintyOptional

    Describe a distribution and its limits of inference.

  3. Data models and grainRequired

    Independently: Define keys, fields and relations for orders and order lines.

  4. SQL joins and constraintsRequired

    Independently: Check primary keys, foreign keys and join results on example tables.

L2

Develop core skills

  1. Extract, transform and loadRequired

    Independently: Map a source, transformation and destination table.

  2. Data quality rulesRequired

    Independently: Specify null, uniqueness, range and total checks.

  3. Incremental loads and idempotencyRequired

    Independently: Design a rerun that does not duplicate the same batch.

L3

Practise scenarios

  1. Research, citation and argumentRequired

    Write a sourced analytical paragraph with limitations.

  2. Logs and lineageRequired

    Independently: Log input rows, output rows and errors for each batch.

  3. Delivery and operations notesOptional

    Independently: Document schema, refresh frequency, reruns and failure recovery.

L4

Build a work sample

  1. Present an inspectable work sampleRequired

    Submit a work sample with method, results and reflection.

Try a practice set

Calculations and choices receive immediate checks. Review open responses yourself against the rubric. All examples are educational.

1. 500 input rows with 10 explicitly rejected. Successful output?
2. How can a batch retry avoid duplication?
3. After the logs and lineage task, state one assumption, one supporting fact and one condition that would change your conclusion.

Pipeline design and quality checklist

For this pipeline design and quality checklist, bound the problem to one educational scenario. List inputs and assumptions, preserve calculations and distinguish findings from missing evidence.

Define the question, scope and success criteria of this pipeline design and quality checklist.

Record data, source links, dates, units and assumptions.

Write reproducible steps with calculations, evidence or a process map.

Separate observations, interpretation and recommendations.

List unknowns, validation steps and conditions that change the conclusion.

Work-sample checklist