JOB DESCRIPTION
About the Role
We are seeking a Platform Engineer with deep expertise in Databricks administration, data governance, and platform‑level engineering standards. This role enables multiple analytics and AI teams to build safely, efficiently, and consistently on a shared Databricks platform by enforcing data quality, ingestion standards, security policies, and cost governance.
You will be the technical owner of platform guardrails, operational stability, access patterns, and cost controls—ensuring the platform scales reliably across business teams.
Key Responsibilities
Platform Administration & Governance
Administer Databricks workspaces, clusters, jobs, Unity Catalog, compute policies, environment configuration, and platform guardrails.
Implement and maintain RBAC and ABAC access controls for secure, compliant data access.
Define and enforce data ingestion standards, naming conventions, schema rules, Delta Lake design patterns, and data quality expectations.
Data Quality & Ingestion Standards
Set platform‑wide standards for ingestion pipelines, Delta architecture, lineage, versioning, and validation.
Review and approve onboarded pipelines for compliance with platform requirements.
Partner with data engineering teams to uplift patterns and enforce consistency.
Security, Compliance & Access Controls
Manage workspace and catalog permissions, row/column‑level policies, attribute‑based filtering, and workspace isolation.
Collaborate with security teams to maintain compliance and enforce global data protection standards.
Cost Management & Monitoring
Implement cost thresholds, alerts, compute policies, and usage dashboards to prevent overspend.
Monitor job and cluster costs, detect anomalies, and recommend optimization actions.
Provide visibility into SKU‑level spend and workspace cost patterns.
Operational Stability & Observability
Ensure platform reliability through automated testing, CI/CD templates, and code governance.
Build dashboards to track code compliance, data access, pipeline health, schema drift, and cost thresholds.
Resolve platform incidents and prevent recurrence by strengthening guardrails and configurations.
Enablement & Best Practices
Define “handrails” for building on the platform: ingestion, Delta conventions, CI/CD, observability, and AI/ML patterns.
Coach data/analytics teams on compliant onboarding and optimal platform usage.
Maintain internal documentation, patterns, code templates, and guidance.
Required Skills & Experience
5+ years in data engineering or platform engineering, with at least 2–3 years in Databricks administration.
Expert knowledge of Unity Catalog, cluster policies, Delta Lake, Spark, workspace configuration, and jobs.
Strong grounding in data governance, data modeling, ingestion frameworks, schema enforcement, versioning, and lineage.
Proven experience implementing RBAC and ABAC in Databricks or similar platforms.
Experience with cost optimization, monitoring, billing logs, and compute governance.
Strong Python/PySpark and SQL skills; familiarity with DLT, Airflow, or Databricks Workflows.
Strong communication skills with ability to set standards and influence teams diplomatically.
Preferred Qualifications
Experience in large-scale enterprise data platforms (Azure/AWS/Google Cloud Platform).
Familiarity with Trading & Supply or other high‑stakes analytical environments.
Experience creating dashboards for governance, cost, compliance, and pipeline health.
Experience with CI/CD, GitHub Actions, Azure DevOps, or similar tools.
Success Indicators
Teams consistently follow ingestion and data standards.
Platform costs stabilized and predictable.
Strong adoption of platform guardrails, templates, and operational dashboards.
Reduced incidents related to access, cost, or ingestion quality.
Never miss an opportunity! Create an alert based on the job you applied for.
