Databricks SME

Scicom Infrastructure Services, Inc. is hiring a Databricks SME to lead the design and implementation of enterprise Databricks Lakehouse architectures. This role is for a senior data engineer or platform architect with hands-on production Databricks experience who can set architectural direction and guide other engineers. The position is based in the United States.

What you will do

• 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 for dev, test, and prod, including shared versus isolated setups
• Ensure architectures meet scalability, availability, and performance requirements
• 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 such as S3, ADLS, GCS, IAM, Key Vault, and networking
• Establish CI/CD pipelines for notebooks, jobs, and infrastructure
• Implement Unity Catalog for centralized governance
• Define data classification, lineage, and audit strategies
• Enforce least-privilege access and secure networking patterns
• Support compliance requirements such as HIPAA, SOC 2, PCI, and GDPR as applicable
• 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
• 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

Well-Architected Framework alignment

You will apply Databricks best practices across all pillars, including security and governance (Unity Catalog, IAM, data access controls), reliability and resilience (job retries, checkpointing, failure isolation), performance efficiency (cluster sizing, autoscaling, caching), cost optimization (compute policies, workload separation, monitoring), and operational excellence (monitoring, automation, CI/CD, runbooks).

What they are looking for

• 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 for governance, lineage, and access controls
• Cloud platforms: AWS, Azure, or GCP
• Infrastructure as Code, with Terraform strongly preferred
• CI/CD tools such as GitHub Actions, Azure DevOps, or GitLab
• Data formats and protocols including Parquet, JSON, and Avro

Certifications

Required:

• Databricks Certified Data Engineer Professional
• Databricks Certified Professional Architect, or an equivalent advanced certification

Preferred:

• 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

• MLflow and MLOps architectures
• Real-time analytics and streaming pipelines
• Multi-region or cross-account data architectures
• Consulting or MSP delivery experience