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