Databricks Architect

Job Summary

As a Databricks Architect, you are responsible for designing, implementing, and governing scalable data and analytics platforms that support enterprise reporting, advanced analytics, and data-driven decision making. You play a hands-on role in shaping data architecture, setting technical standards, and ensuring Databricks solutions are built for performance, reliability, and long-term scalability. You work closely with data engineers, analysts, and business stakeholders to guide solution design, review implementations, and ensure alignment with business objectives. You are actively involved in platform optimization, cost management, and the adoption of best practices across data ingestion, transformation, and analytics. As a technical leader, you mentor team members, influence architectural decisions, and help mature the organization s overall data capabilities.

Responsibilities

• Design and architect end-to-end data platforms using the Databricks Lakehouse architecture.

• Define and implement scalable batch and streaming data pipelines using Apache Spark and Delta Lake.

• Partner with engineering, analytics, and business teams to translate functional requirements into technical designs.

• Establish and enforce data modeling, performance optimization, and cost-management best practices within Databricks.

• Lead cloud-based implementations across AWS, Azure, or GCP, including networking, security, and access controls.

• Provide technical leadership through architecture reviews, code reviews, and design sessions.

• Support production environments by troubleshooting data quality, performance, and reliability issues.

  Skills Experience

• 10+ years of overall experience in data engineering, data architecture, or enterprise analytics platforms.

• Hands-on experience designing and implementing solutions on Databricks.

• Strong proficiency in Apache Spark, SQL, and Delta Lake for large-scale data processing.

• Experience building and optimizing both batch and real-time data pipelines.

• Proven experience working within at least one major cloud platform, including AWS, Azure, or GCP.

• Solid understanding of data governance concepts including security, lineage, access control, and compliance.

• Experience with data modeling techniques for analytical and reporting workloads.

• Familiarity with CI/CD practices, version control, and infrastructure-as-code approaches for data platforms.

• Demonstrated ability to mentor engineers and lead technical design discussions in cross-functional teams.

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