Databricks SME
Key Responsibilities: Architecture & Platform Design Design enterprise Databricks Lakehouse architectures aligned with the Databricks Well-Architected Framework Define reference architectures for batch, streaming, analytics, and ML workloads Select and standardize cluster, compute, and workspace architectures Design multi-workspace strategies (dev/test/prod, shared vs. isolated) Ensure architectures meet scalability, availability, and performance requirements Well-Architected Framework Alignment Apply Databricks best practices across all pillars, including: Security & Governance (Unity Catalog, IAM, data access controls) Reliability & Resilience (job retries, checkpointing, failure isolation) Performance Efficiency (cluster sizing, autoscaling, caching) Cost Optimization (compute policies, workload separation, monitoring) Operational Excellence (monitoring, automation, CI/CD, runbooks) Implementation & Engineering Lead Databricks workspace, cluster, and Unity Catalog implementations Implement Delta Lake, Delta Live Tables (DLT), and Structured Streaming Build and optimize ETL/ELT pipelines using Spark and SQL Integrate Databricks with cloud services (S3/ADLS/GCS, IAM, Key Vault, networking) Establish CI/CD pipelines for notebooks, jobs, and infrastructure Security, Governance & Compliance Implement Unity Catalog for centralized governance Define data classification, lineage, and audit strategies Enforce least-privilege access and secure networking patterns Support compliance requirements (HIPAA, SOC 2, PCI, GDPR as applicable) Operations & Optimization Monitor platform health, performance, and cost Troubleshoot production issues across jobs, clusters, and data pipelines Perform workload tuning and cost-performance optimization Define SLOs, alerts, and operational metrics Collaboration & Leadership Partner with Data Engineering, Analytics, ML, Platform, and Security teams Translate business requirements into technical architectures Provide architectural guidance and technical mentorship Communicate risks, tradeoffs, and recommendations to leadership Required Qualifications: Experience 7+ years in data engineering, analytics, or platform architecture 3–5+ years hands-on Databricks experience in production environments Proven experience applying the Databricks Well-Architected Framework Experience designing cloud-native lakehouse architectures Experience supporting mission-critical data platforms Technical Skills Databricks Lakehouse Platform Apache Spark (PySpark / Scala / Spark SQL) Delta Lake, Delta Live Tables, Structured Streaming Unity Catalog (governance, lineage, access controls) Cloud platforms: AWS, Azure, or GCP Infrastructure as Code (Terraform strongly preferred) CI/CD tools (GitHub Actions, Azure DevOps, GitLab, etc.) Data formats and protocols (Parquet, JSON, Avro) Certifications Required: Databricks Certified Data Engineer Professional Databricks Certified Professional Architect (or equivalent advanced certification) Preferred / Additional Certifications AWS Certified Solutions Architect (Associate or Professional) Azure Solutions Architect Expert Google Professional Data Engineer Databricks Machine Learning Professional Snowflake or other cloud data platform certifications Soft Skills Strong architectural decision-making and documentation skills Excellent communication with technical and non-technical stakeholders Ability to lead design reviews and architecture governance forums Strong troubleshooting and performance-tuning mindset
Nice-to-Have
Experience MLflow and MLOps architectures Real-time analytics and streaming pipelines Multi-region or cross-account data architectures Consulting or MSP delivery experience