Support day-to-day operations for cloud data platforms (workspace/project setup, access requests, basic configuration, troubleshooting).
Assist with platform hygiene: organizing environments, documenting standards, and improving repeatability.
Help implement and maintain guardrails (naming/tagging conventions, access patterns, basic cost awareness).
Contribute to CI/CD workflows for data and analytics assets (pipelines, jobs, semantic models, reports), under mentorship.
Help maintain reusable templates/checklists for deployments (approvals, promotion steps, rollback notes, release documentation).
Assist teams with onboarding and “how-to” enablement across a core platform (Fabric or Snowflake or Databricks) plus a BI tool (Power BI or Tableau).
Support basic performance triage (query/job failures, refresh issues, common workspace capacity constraints) and escalate effectively when needed.
Help build and maintain runbooks (standard operating procedures, known issues, quick fixes, escalation paths).
Participate in incident response support (triage, notes, follow-ups) and contribute to preventative improvements.
Follow and reinforce least-privilege access practices and secure secret handling (e.g., Key Vault/secret scopes where applicable).
Assist with data governance basics (PII handling expectations, row-level access concepts, environment separation).

Minimum 2 years’ experience in a data engineering, platform engineering, analytics engineering, or cloud operations role (internships/co-ops count).
Comfort with one or more of the following areas (not all): A cloud data platform: Snowflake (preferred) or Microsoft Fabric or Databricks
A BI platform: Power BI or Tableau
CI/CD concepts (Git, branching/PRs, basic pipelines)
Basic scripting capability in Python or PowerShell (or willingness to learn quickly).
Strong troubleshooting mindset: can break problems down, gather evidence, and communicate clearly.
Ability to document what you learned (runbooks, checklists, short “how-to” guides).
Exposure to cloud concepts: identity/access, resource organization, logging/monitoring.
Familiarity with SQL and performance basics (indexes/partitioning concepts, query plans at a high level).
Understanding of data governance concepts (PII, RLS/CLS, environment separation).
Any relevant certs (Azure fundamentals, Snowflake, Databricks, etc)
Demonstrated experience with AI/ML/GenAI enablement (model lifecycle, AI Search, Azure OpenAI integration, or MLOps).

Data & AI Platform Engineer

Key skills