Azure enterprise principal architect — KR elixir/ SYNOVUS- atlanta hybrd
Platform & Technology Leadership
▸ Serve as the principal architect for Databricks — Unity Catalog, Delta Live Tables, Workflows, and the emerging LTAP (Lakebase) architecture for AI agent workloads.
▸ Architect real-time and batch data pipelines using Databricks, dbt, and streaming technologies (Apache Kafka / Spark Structured Streaming).
▸ Evaluate and govern the adoption of complementary tools in the modern data stack (Fivetran, dbt, data catalog, reverse ETL, semantic layer).
▸ Define cloud infrastructure architecture patterns on Azure or AWS supporting the Databricks environment, ensuring scalability, cost governance, and HA/DR.
Data Governance & Compliance
▸ Architect enterprise data governance frameworks aligned with banking regulations: BSA/AML, CCPA, GLBA, Basel III/IV, and OCC model risk guidelines.
▸ Design and implement data lineage, data quality, and master data management (MDM) capabilities across the merged institution.
▸ Partner with Legal, Risk, and Compliance to ensure data handling architecture meets audit, privacy, and regulatory reporting requirements.
▸ Establish data classification and access control patterns within Unity Catalog to enforce row-level and column-level security at scale.
Required Qualifications
▸ Experience at a bank, fintech, or financial services firm with $50B+ in assets.
▸ Familiarity with banking data domains: core banking systems, loan origination, deposit systems, general ledger, and regulatory reporting (CCAR, DFAST, Call Report).
▸ Databricks Certified Data Engineer Associate/Professional or equivalent certification.
▸ Experience with dbt (data build tool), Apache Kafka, or Confluent for streaming data pipelines.
▸ Exposure to AI/ML platform architecture — MLflow, Feature Store, AI agents in Databricks.
▸ 10+ years of data architecture, data engineering, or data platform experience in large, complex organizations.
▸ 5+ years of hands-on experience with Databricks — including Delta Lake, Unity Catalog, Databricks SQL, and Databricks Workflows.
▸ Deep expertise in data modeling: dimensional modeling, Data Vault 2.0, entity-relationship design, and domain-driven data products.
▸ Strong proficiency in SQL and Python; familiarity with PySpark and Databricks notebooks.
▸ Experience architecting lakehouse solutions on cloud platforms (Azure preferred; AWS acceptable).
▸ Demonstrated experience with data governance frameworks — data lineage, catalog, quality, MDM, and access control.
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