Define and evolve the technical vision and roadmap for APFM's Databricks platform and broader data ecosystem.
Drive the evolution of APFM's federated data operating model, balancing domain ownership with enterprise governance and standards.
Partner with Analytics Engineering and business stakeholders to evolve the semantic layer, ensuring business definitions, metrics, and dimensions remain consistent, discoverable, and trusted.
Establish standards for metadata management, lineage, discoverability, stewardship, and data quality across the organization.
Drive development of reusable data products and shared platform capabilities that enable self-service analytics at scale.
Lead adoption and governance of Databricks capabilities including Delta Lake, Unity Catalog, Workflows, AI/BI Genie, MLflow, Vector Search, and related platform services.
Partner closely with AI and Machine Learning teams to establish scalable operational patterns for model development, deployment, monitoring, and governance.
Guide technical decision-making across multiple teams and domains, helping align platform investments with business outcomes.
Mentor engineers across the organization and help elevate technical capabilities and engineering excellence.

10+ years of experience in data engineering, data platform architecture, governance, or related disciplines.
Deep expertise designing and operating enterprise-scale Databricks Lakehouse platforms.
Extensive experience with Spark, Delta Lake, Unity Catalog, Databricks Workflows, and platform governance capabilities.
Strong proficiency in SQL, Python, and PySpark, with experience building and optimizing data processing workloads on Databricks.
Experience designing and supporting semantic layers, metrics governance, and self-service analytics capabilities.
Strong understanding of governance, metadata management, lineage, observability, security, and reliability engineering principles.

Principal Data Engineer, Data Platform

Key skills