Data & AI Platform Engineer

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We deliver reporting, analytics, warehousing, and targeted ML/AI/GenAI solutions across Microsoft Cloud (Azure, M365, Dynamics, Power Platform) and hybrid/on-prem environments. We partner with application teams and systems engineering to run secure, reliable, and well-governed platforms.

This is an early-career engineering role focused on building, operating, and improving cloud data/analytics platforms (e.g., Microsoft Fabric, Snowflake, Databricks) and supporting BI delivery (e.g., Power BI, Tableau). You’ll contribute to platform reliability, CI/CD, environment setup, and operational runbooks—while learning best practices in security, governance, and deployment automation from senior engineers.

• 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 elevate 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 Power Shell (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).
• Ability to work…