Role Overview
As a Senior Databricks Data Platform Engineer, you will own the engineering, automation, and day-to-day health of our enterprise Databricks Lakehouse platform. This is a platform-first role: rather than building individual business pipelines, you will build the foundation that every data engineer, analyst, and data scientist depends on, including workspaces, compute, Unity Catalog governance, deployment pipelines, and the guardrails that keep the platform secure, reliable, and cost-efficient.
You will treat the platform as a product. That means codifying infrastructure and configuration, delivering changes through fully automated CI/CD, introducing agentic AI workflows that reduce manual operational effort, and continuously driving down compute and storage cost while improving performance. You will combine deep hands-on Databricks expertise with a FinOps and DevOps mindset, and you will measure your success in platform reliability, developer productivity, and dollars saved.
This is a highly collaborative role. You will partner with data engineering, data science, security, cloud infrastructure, and finance teams to set platform standards, guide adoption of new Databricks capabilities, and mentor engineers across the organization.
About the role
Databricks Platform Engineering & Administration
- Own the architecture, configuration, and lifecycle of client's Databricks environments across development, test, and production, including workspaces, account-level settings, networking, and identity integration
- Administer and evolve Unity Catalog, including metastores, catalogs, schemas, external locations, storage credentials, and fine-grained access controls
- Design and enforce compute strategy across all-purpose clusters, job clusters, serverless compute, SQL warehouses, instance pools, and cluster policies
- Manage platform upgrades, Databricks Runtime adoption, feature previews, and deprecations with minimal disruption to consuming teams
- Build reusable platform templates, starter projects, and self-service capabilities that let engineering teams onboard quickly and consistently
CI/CD & Platform Automation
- Design, build, and maintain CI/CD pipelines for Databricks code and configuration using Databricks Asset Bundles, Git folders, and Azure DevOps or GitHub Actions
- Codify platform infrastructure with Terraform (Databricks and cloud providers), ensuring every workspace, cluster policy, permission, and Unity Catalog object is version-controlled and reproducible
- Implement automated testing, code quality gates, security scanning, and promotion workflows from development through production
- Establish branching, release, and rollback standards for notebooks, jobs, Delta Live Tables / Lakeflow pipelines, and ML assets
- Automate routine administration such as user and group provisioning (SCIM), secret management, token rotation, and environment drift detection
Agentic Workflows & AI-Enabled Operations
- Design and deploy agentic workflows that automate platform operations, such as job failure triage, root-cause analysis, cost anomaly investigation, and remediation recommendations
- Build AI agents and tools on Databricks (Mosaic AI Agent Framework, Model Serving, Vector Search) and integrate them with enterprise systems through Model Context Protocol (MCP)
- Use Copilot and Anthropic models to accelerate infrastructure-as-code, pipeline development, code review, and documentation
- Establish guardrails, evaluation, observability, and human-in-the-loop controls so agentic automation is safe, auditable, and trusted
- Partner with data science and engineering teams to provide a governed platform foundation for GenAI, RAG, and ML workloads
Platform Cost Optimization (FinOps)
- Own Databricks cost visibility and accountability using system tables (billing, usage, compute, and query history), tagging standards, and chargeback or showback reporting
- Identify and deliver savings through cluster right-sizing, auto-termination, autoscaling tuning, spot and reserved capacity strategies, Photon and serverless evaluation, and workload consolidation
- Enforce cost guardrails through cluster policies, budget alerts, and compute governance that prevent waste without slowing teams down
- Build dashboards and automated alerts that surface cost anomalies, idle resources, and optimization opportunities to engineering and finance stakeholders
- Partner with finance and procurement on DBU commitments, forecasting, and capacity planning
Storage & Performance Optimization
- Define and enforce Delta Lake storage standards, including file sizing, liquid clustering, Z-ordering, partitioning strategy, and deletion vectors
- Automate table maintenance using predictive optimization, OPTIMIZE, VACUUM, and retention policies to control storage growth and improve query performance
- Manage cloud object storage (ADLS Gen2 / S3) layout, lifecycle and tiering policies, and data retention aligned with business and compliance requirements
- Tune Spark and SQL workloads by analyzing query profiles, shuffle, skew, caching, and Photon usage to improve speed and reduce cost
- Identify and remediate small-file problems, orphaned data, unused tables, and redundant copies across the Lakehouse
Governance, Security & Compliance
- Implement secure-by-default platform controls, including private networking, IP access lists, encryption, secrets management, and least-privilege access
- Ensure auditable data access and lineage through Unity Catalog, audit logs, and system tables, with appropriate handling of PII and regulatory requirements
- Apply data classification, row- and column-level security, and data masking standards across catalogs
- Partner with security and compliance teams on platform reviews, vulnerability remediation, and audit evidence
Reliability & Operational Excellence
- Define platform SLOs and build monitoring, alerting, and observability for jobs, clusters, warehouses, and pipelines
- Lead incident response, root-cause analysis, and post-incident improvements for platform issues, and participate in on-call rotations
- Create clear runbooks, standards, and platform documentation that enable self-service and reduce support load
- Continuously evaluate new Databricks and cloud capabilities and lead proofs of concept that improve the platform
- Mentor engineers, run enablement sessions, and champion platform best practices across McAfee
About you
You are a hands-on platform engineer who thinks in terms of automation, reliability, and unit economics. You enjoy building the systems that make other engineers productive, you are not satisfied until a manual process is codified, and you can explain the cost and performance impact of a platform decision to both engineers and business leaders.
Required Qualifications
- 7+ years of experience in data engineering, data platform, or DevOps roles, including 3+ years administering and engineering Databricks in an enterprise environment
- Deep expertise in Databricks platform administration, including Unity Catalog, workspace and account configuration, cluster policies, SQL warehouses, and Databricks Workflows
- Proven experience building CI/CD for data platforms using Databricks Asset Bundles, Terraform, and Azure DevOps, GitHub Actions, or a similar tool
- Demonstrated track record of reducing platform cost through compute right-sizing, policy enforcement, and usage analytics, with measurable results
- Strong knowledge of Delta Lake internals and storage optimization, including file compaction, clustering, partitioning, and table maintenance
- Strong programming skills in Python and SQL, with working knowledge of PySpark and Spark performance tuning
- Hands-on experience with at least one major cloud platform (Azure, AWS, or GCP), including networking, identity, and object storage
- Experience securing platforms that handle sensitive data, including PII, with attention to compliance and governance
Preferred Qualifications
- Experience building agentic or GenAI workflows using Mosaic AI, LangChain, LangGraph, or similar frameworks
- Practical knowledge of Model Context Protocol (MCP) to connect enterprise applications and automate platform workflows
- Familiarity with Copilot and Anthropic models to enhance development speed, code quality, and documentation
- Databricks certifications such as Data Engineer Professional or Platform Administrator
- Experience with Databricks system tables, Lakehouse Monitoring, and building FinOps dashboards
- Familiarity with Lakeflow / Delta Live Tables, Databricks SQL, Model Serving, and MLOps practices
- Knowledge of containerization and orchestration technologies (Docker, Kubernetes)
- Experience migrating workloads from legacy platforms (Hadoop, Snowflake, or on-premises warehouses) to Databricks
Personal Attributes
- Automation-first mindset, with a habit of eliminating toil and codifying repeatable work
- Cost-conscious and data-driven, consistently tying platform decisions to measurable business value
- Strong ownership and accountability for platform reliability and the experience of the teams who depend on it
- Excellent communication skills, with the ability to influence standards across technical and non-technical stakeholders
- Proactive problem-solver with strong analytical and troubleshooting skills
- Passionate about emerging technologies, especially in AI, automation, and the Lakehouse ecosystem
- Committed to mentoring and knowledge-sharing that elevates the wider engineering team
