Data Architect

We are looking for a senior, hands-on Databricks Architect to lead the design and delivery of an enterprise data Lakehouse. The role will establish a secure, scalable platform and drive adoption of Databricks capabilities across data engineering, governance, analytics, and AI readiness.

Key Responsibilities

• Own lakehouse architecture and implementation roadmap, including workspaces, cloud integration, storage, networking, identity, and compute.
• Design batch, streaming, and CDC pipelines using Delta Lake, Auto Loader, and Lakeflow, with layered data architecture, modeling, data quality, and reliable incremental processing.
• Establish Unity Catalog governance covering access controls, sensitive-data protection, lineage, discovery, and auditability.
• Enable Databricks SQL, BI integration, curated data products, and governed business metrics; assess Genie and AI/ML capabilities against business needs.
• Lead performance and cost optimization, CI/CD, infrastructure automation, monitoring, failure recovery, and disaster recovery.
• Guide client stakeholders and engineering teams through architecture decisions, implementation reviews, production readiness, and knowledge transfer.
• Proven ownership of enterprise Databricks platforms delivered into production.
• Strong hands-on expertise in Spark, PySpark, Python, SQL, Delta Lake, Unity Catalog, and pipeline orchestration.
• Experience with lakehouse modeling, streaming, CDC, schema evolution, deduplication, and safe pipeline recovery.
• Strong AWS or Azure expertise, including platform security, networking, storage, and enterprise integration.
• Demonstrated performance troubleshooting, cost optimization, automated testing, CI/CD, and infrastructure-as-code experience.
• Strong client-facing communication and technical leadership, with clear examples of architecture trade-offs and production outcomes.

Preferred

• Databricks Data Engineer Professional certification.
• Experience with serverless compute, Terraform, Declarative Automation Bundles, and warehouse migration.
• Exposure to AI/BI Genie, MLflow, model serving, vector search, or RAG on governed enterprise data.

Ideal Candidate

A hands-on architect who can build the lakehouse foundation, lead delivery, and expand Databricks adoption in line with business priorities.

• Strong hands-on expertise in Spark, PySpark, Python, SQL, Delta Lake, Unity Catalog, and pipeline orchestration.
• Experience with lakehouse modeling, streaming, CDC, schema evolution, deduplication, and safe pipeline recovery.
• Strong AWS or Azure expertise, including platform security, networking, storage, and enterprise integration.
• Demonstrated performance troubleshooting, cost optimization, automated testing, CI/CD, and infrastructure-as-code experience.
• Strong client-facing communication and technical leadership, with clear examples of architecture trade-offs and production outcomes.

Preferred

• Databricks Data Engineer Professional certification.
• Experience with serverless compute, Terraform, Declarative Automation Bundles, and warehouse migration.
• Exposure to AI/BI Genie, MLflow, model serving, vector search, or RAG on governed enterprise data.

Ideal Candidate

A hands-on architect who can build the lakehouse foundation, lead delivery, and expand Databricks adoption in line with business priorities.