Data Platform Architect DataBricks

Key Responsibilities: Define target-state architecture for enterprise data platforms and cloud-native analytics solutions. Lead large-scale data migrations and modernization initiatives from on-premises platforms to cloud environments. Design scalable, resilient, secure, and highly available data architectures capable of supporting petabyte-scale workloads. Design and implement Medallion and lakehouse architectures. Establish architectural standards, governance frameworks, and engineering best practices. Partner with infrastructure, security, application, and architecture teams to align data platform initiatives with enterprise cloud strategies. Evaluate and recommend technologies for data storage, processing, streaming, governance, and observability. Provide technical leadership and mentorship to engineering teams implementing Spark, Kafka, Databricks, and cloud-native services. Drive performance optimization, cost management, scalability, and operational excellence across the data platform. Support migration roadmaps, risk assessments, and modernization strategies for executive stakeholders. Design and support large-scale data movement and transport solutions. Apply data warehousing principles, including schema design, star and snowflake schemas, fact and dimension modeling, and Slowly Changing Dimensions (SCDs).

Required Qualifications: 10+ years of experience in data platform architecture, data engineering, or distributed systems. Demonstrated experience leading enterprise-scale cloud migration and modernization initiatives involving hundreds of terabytes or petabytes of data. Experience with large-scale data migration from platforms such as Vertica, Hadoop, Teradata, Oracle, Snowflake, or similar technologies. Strong experience designing and implementing greenfield large-scale data platforms. Strong expertise in Medallion architecture. Strong experience with big data movement and transport. Strong understanding of data warehousing fundamentals, including star and snowflake schemas, fact and dimension modeling, and SCDs. Strong experience with Databricks and Apache Spark. Strong experience with Apache Kafka and distributed data processing. Experience with AWS, Azure, or GCP cloud platforms. Strong software engineering background with Java and Python programming experience. Strong SQL skills and experience with object-oriented programming concepts. Experience designing highly available, scalable, and resilient distributed systems. Strong understanding of data governance, security, compliance, and operational monitoring. Experience with enterprise messaging or data transport technologies such as AMPS, MQ, Hadoop, or similar platforms.

Preferred Qualifications: Experience implementing lakehouse architectures. Experience designing streaming and real-time analytics platforms. Experience working within highly regulated financial services or enterprise environments. Master's degree. Experience with Databricks and cloud architecture best practices.

Certifications: Databricks certification(s) preferred. AWS Solutions Architect certification(s) preferred.