Azure Enterprise Principal Architect_Atlanta,GA

Azure enterprise principal architect
Atlanta,GA

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.