Lead Data Platform Engineer

Job Description

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.

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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.

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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.

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Responsibilities

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

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• 12+ years of experience across data engineering, cloud data platforms, data warehousing, enterprise data architecture, or related disciplines.
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• Strong hands-on Python experience from production data-engineering projects.
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• Strong hands-on PySpark experience from production data-engineering projects.
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• Advanced SQL experience.
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• Experience with Databricks, Snowflake, or comparable modern enterprise data platforms.
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• Strong understanding of data warehouses, data lakes, lakehouse platforms, and scalable cloud data architecture.
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• Experience with ingestion, ETL, transformation, loading, data movement, storage, orchestration, and consumption patterns.
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• Ability to troubleshoot complex platform and data-delivery issues.
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• Experience supporting reusable data frameworks, shared data platforms, or enterprise engineering standards.
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• Experience onboarding business or technology teams onto a shared platform.
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• Experience driving framework adoption and consistent delivery across multiple teams.
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• Strong understanding of data quality, lineage, metadata, logging, governance, and auditability.
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• Ability to review technical designs and code while asking detailed, practical engineering questions.
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• Experience creating technical documentation, onboarding processes, training materials, or implementation playbooks.
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• Strong stakeholder-management and communication skills.
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• Ability to translate technical concepts for business and domain users.
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• Experience leading or mentoring data engineers or technical resources.
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• Ability to combine technical leadership, stakeholder engagement, platform enablement, and hands-on troubleshooting.
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• Ability to work according to the required New York City hybrid schedule.
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Nice to Have

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• Databricks expertise.
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• Snowflake experience.
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• Financial-services or banking experience.
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• Capital-markets, risk, treasury, or regulatory-data experience.
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• Experience working with regulated or sensitive enterprise data.
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• Experience leading a small data-engineering team.
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• Experience establishing a data-platform Center of Excellence.
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• Experience developing platform operating models, adoption metrics, governance controls, or support standards.
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• Experience enabling self-service data-platform adoption.
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• Background in enterprise cloud-data transformation or platform modernization.
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• Experience working across globally distributed teams.
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