Data and Analytics Intern II

In this role, you will partner with Data & Analytics and adjacent stakeholders to strengthen how data assets are organized, governed, and documented in Databricks. You’ll help define and apply practical metadata standards (ownership, classification, lifecycle, etc.), document repeatable operating processes, and support at least one AI/ML initiative by contributing to data preparation and model experimentation workflows.

Responsibilities may include

Define and apply metadata tagging standards for priority Databricks assets (e.g., owner/steward, domain, sensitivity/classification, refresh cadence, lifecycle stage).

Improve data discoverability and consistency through naming conventions and catalog organization.

Create concise, usable process documentation (SOPs, checklists, templates, runbooks) for common Databricks workflows.

Support governance-related workflows (intake/onboarding steps, access patterns, documentation for “definition of done”).

Contribute to AI/ML project work on Databricks (data prep, feature engineering support, experimentation, evaluation summaries).

Produce handoff-ready deliverables and recommendations for next-step improvements.

Skills and abilities

Working knowledge of Python and SQL.

Familiarity with modern data platform concepts (ETL/ELT, data modeling basics, data quality concepts).

Ability to translate ambiguous workflows into clear, actionable documentation.

Strong communication and collaboration skills; able to work with technical and non-technical stakeholders.

Preferred: exposure to Databricks and/or data governance concepts (catalogs, classification, ownership, access controls); familiarity with ML experimentation tooling.