Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
• Strong experience leading the design and implementation of enterprise cloud data platforms, with substantial hands-on Databricks experience.
• Strong working knowledge of Apache Spark, Delta Lake, Databricks Workflows, Unity Catalog, SQL, and Python. Scala experience is beneficial.
• Demonstrated ability to perform complex data analysis, profiling, reconciliation, debugging, performance analysis, and root-cause investigation using large datasets.
• Experience implementing production-grade batch, streaming, micro-batch, event, and file-based ingestion patterns, including schema evolution, replay, backfill, and idempotent processing.
• Experience with cloud-native data services, object storage, IAM, private networking, key management, logging, monitoring, infrastructure as code, and CI/CD. AWS experience is preferred; Azure or Google Cloud Platform experience is also relevant.
• Strong knowledge of data security and governance concepts, including least privilege, identity federation, service principals, classification, lineage, retention, masking, audit logging, DLP, controlled data sharing, and regulated or customer-sensitive data handling.
• Experience designing or operating dedicated-tenant, multi-tenant, or customer-isolated platforms, including tenant lifecycle, logical and physical isolation, resource governance, and cross-tenant security testing.
• Experience implementing encryption in transit and at rest, cloud KMS or HSM integrations, customer-managed keys, BYOK, key rotation, separation of duties, and cryptographic control evidence.
• Strong architecture, technical documentation, stakeholder management, and engineering leadership skills, with the ability to convert ambiguous requirements into executable designs and delivery plans.