Lead Data Platform Engineer

A global financial institution is seeking a Lead Data Platform Engineer to drive adoption and enablement for a strategic enterprise cloud data platform. This position sits within a centralized data technology organization responsible for shared data-platform capabilities, engineering frameworks, governance guardrails, data quality, lineage, and consistent delivery across business and technology domain teams.

This is not a heads-down development position or a traditional solutions-architecture role. The ideal candidate has substantial prior hands‑on data‑engineering experience and can now operate as a technical enablement leader across platform onboarding, framework adoption, documentation, training, troubleshooting, stakeholder engagement, and delivery standards.

The successful candidate will remain technically credible enough to review engineering work, understand platform architecture, diagnose complex issues, and guide design decisions while leading a small team responsible for helping domain teams adopt and use enterprise data capabilities successfully.

• Lead enablement and onboarding for domain teams adopting a shared enterprise data platform.
• Manage and mentor a small team of engineers supporting platform adoption and Center of Excellence activities.
• Guide teams through platform intake, environment configuration, access, permissions, documentation, and delivery methodology.
• Promote adoption of reusable data‑engineering frameworks, shared services, common components, and standardized delivery practices.
• Partner with data engineers, architects, product teams, business users, and domain stakeholders to understand platform requirements and resolve adoption challenges.
• Provide technical guidance across ingestion, transformation, loading, data movement, orchestration, logging, storage, and consumption patterns.
• Troubleshoot platform usage, ETL, access, permissions, data quality, lineage, logging, orchestration, and production‑delivery issues.
• Review technical work and help teams make sound data‑engineering and platform decisions.
• Improve onboarding playbooks, intake forms, technical documentation, support procedures, and training materials.
• Translate recurring user challenges into platform, framework, process, or documentation improvements.
• Establish delivery standards that improve consistency, scalability, maintainability, and auditability.
• Promote strong practices across data quality, lineage, metadata, governance, access control, and regulated enterprise data.
• Support self‑service and semi‑self‑service platform adoption while maintaining appropriate governance guardrails.
• Define or improve adoption metrics, support models, platform processes, and Center of Excellence operating practices.
• Collaborate across globally distributed engineering and stakeholder teams.
• Balance immediate onboarding and troubleshooting needs with longer‑term platform‑adoption strategy.

• 12+ years of experience across data engineering, cloud data platforms, data warehousing, enterprise data architecture, or related disciplines.
• Strong hands‑on Python experience from production data‑engineering projects.
• Strong hands‑on PySpark experience from production data‑engineering projects.
• Advanced SQL experience.
• Experience with Databricks, Snowflake, or comparable modern enterprise data platforms.
• Strong understanding of data warehouses, data lakes, lakehouse platforms, and scalable cloud data architecture.
• Experience with ingestion, ETL, transformation, loading, data movement, storage, orchestration, and consumption patterns.
• Ability to troubleshoot complex platform and data‑delivery issues.
• Experience supporting reusable data frameworks, shared data platforms, or enterprise engineering standards.
• Experience…