Databricks Data Engineering Architect

Databricks Data Engineering Architect Primary Responsibilities Define and drive the

Databricks Data Engineering architecture and strategy , aligned with enterprise Data & Analytics standards. Take

end-to-end ownership

of Databricks solutions from architecture and design through implementation, production deployment, support, and optimization. Define and enforce

policy-based governance and cost controls

across Databricks workloads, including Apps, Agents, compute, AI/ML resources, and

AI Gateway . Establish and enforce

RBAC and least-privilege security

across Unity Catalog, Databricks Apps, Agents, AI Gateway, service principals, and Dev/QA/Production environments. Design reusable data ingestion and transformation frameworks using

Databricks, Spark, Delta Lake, SQL, Python/PySpark, and Lakeflow . Architect scalable

Lakehouse solutions on Databricks and AWS

supporting batch, streaming, near-real-time, analytics, and AI use cases. Design and support

Agentic AI and Databricks Apps development , including architecture, deployment, integration, security, monitoring, and production readiness. Establish and enforce

Data Engineering Governance and Security

using Unity Catalog, including RBAC, least privilege, service principals, data access controls, lineage, auditing, and data quality. Define engineering and governance standards across

Dev, QA, and Production , including Git , Branches etc. Design data integration patterns for

SAP/ERP, databases, APIs, files, streaming platforms, and cloud services . Design and evaluate data models and serving patterns for

BI, analytics, Data Science, AI, applications, and downstream consumers . Establish standards for Databricks

compute, performance optimization, monitoring, reliability, and cost management . Proactively identify and resolve architecture, security, governance, performance, and production issues. Work closely with

Security, Cloud, Infrastructure, Data Governance, Data Science, BI, application, and business teams

to deliver enterprise solutions. Provide technical leadership, architecture reviews, hands-on prototyping, engineering standards, and mentoring. Demonstrate a strong ownership mindset by independently driving initiatives, managing dependencies and risks, and ensuring successful delivery. You Must Have 9+ years

of Data Engineering/Data Architecture experience with

4+ years of hands-on Databricks experience . Strong experience with

Databricks, Delta Lake, Spark, SQL, Python/PySpark, and Lakehouse architecture . Strong knowledge of

Unity Catalog, Data Governance, Data Security, RBAC, lineage, auditing, data quality, and access controls . Experience designing enterprise

batch, streaming, and reusable data pipelines . 1+ years of Experience with

Databricks AI/BI, Genie, Apps, and other emerging services

. Strong AWS experience, including

S3, Lambda, Glue, CloudWatch, RDS, and Redshift . Strong experience in

Git, Dev/QA/Production deployment patterns Strong understanding of

data modeling, performance optimization, scalability, reliability, security, and cost management . Proven ability to

own complex technical initiatives , solve production issues, and work effectively across multiple technical and business teams. Excellent communication, architecture, problem-solving, and technical leadership skills. Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related field. Nice to Have Databricks and/or AWS certifications. Experience with

dbt, Sigma, Tableau, or Power BI . Experience with enterprise

FinOps, cost/usage monitoring, and platform governance . SAP integration or functional experience.