Explain where the data comes from and who uses it
Start with where data originates, how it enters the platform, how it changes and who depends on the result. Batch jobs, events and streaming systems have different timing and failure characteristics. An engineer who understands these constraints can choose a sensible processing pattern instead of introducing complexity by default.
Explain how you handled late, duplicate or missing records; schema evolution; reruns; and access to sensitive fields. A simple architecture diagram with names removed can communicate far more than a list of pipeline tools.
- What was the source and how often did it change?
- What would downstream users notice if this job failed or produced incomplete data?
- How did you verify freshness, correctness and recoverability?
Separate platform use from transformation skill
Databricks lakehouse patterns often describe progressive refinement of data. Snowflake is commonly used for governed data and analytical workloads. The important question is what you built within the platform, not whether one product title appears on a résumé.
SQL remains central to many data engineering roles. dbt can help organize transformations as documented, tested models; PySpark can be appropriate when distributed processing is justified. Explain joins, partitioning, testing or model definitions you actually worked with. Familiarity with a vendor interface does not automatically imply expertise in every underlying system.
- Describe the transformation logic and why it belonged at that layer.
- Connect any dbt tests or data checks to the specific failure they detect.
- If you used PySpark, explain a performance or data-volume decision without exposing private workloads.
Show reliability and data ownership
Useful pipelines need clear ownership. Teams benefit from documented source definitions, expectations for consumers and alerts that identify actionable failures. Access rules and retention choices belong in the design discussion, even when another team makes the final governance decision.
Do not present a clean demonstration dataset as proof of production-scale operations. Say whether you built a new pipeline, maintained an existing one, modeled a reporting layer or supported a migration. Shared work is still valuable when the boundaries are clear.
Describe a real data engineering project
In your Talent Passport, choose one non-confidential data journey. Set out the problem, your role, the tools, a quality or recovery challenge and the evidence used to check the outcome. If you cannot share metrics, describe the method and what the team learned instead of inventing impact numbers.
Keep this example current. A person who once used Snowflake for a small analysis and now operates a Databricks pipeline should describe those as different experiences. That honesty makes it easier to match real requirements to relevant expertise.
Turn this guide into your profile
- Create an account and verify your email.
- Complete your profile; you can optionally upload a .pdf or .docx resume during setup, and manage resumes later in your Talent Passport.
- Keep your Talent Passport current as your work changes.