Lead Data Platform Engineer, Enablement & COE

Madison-Davis; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Madison-Davis, LLC is seeking a Lead Data Platform Engineer to drive adoption and enablement for a strategic enterprise cloud data platform. This role sits in a centralized data technology organization responsible for shared data-platform capabilities, governance guardrails, data quality, lineage, and consistent delivery across business and technology teams.

You will lead a small team, mentor engineers, guide platform onboarding, and develop documentation, training, and playbooks.

Qualifications

• 12+ years of experience across data engineering, cloud data platforms, data warehousing, 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 leading or mentoring data engineers or technical resources.
• Ability to translate technical concepts for business and domain users.

Responsibilities

• Lead enablement and onboarding for domain teams adopting a shared enterprise data platform.
• Mentor a small team of engineers 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.

Skills

Python

PySpark

SQL

Stakeholder management

Communication

Mentoring

Education

Bachelor's degree in CS or related field

Tools

Databricks

Snowflake

Apache Airflow

ETL pipelines

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. 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.

RESPONSIBILITIES

• 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.

ROLE REQUIREMENTS

• 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 onboarding business or technology teams onto a shared platform.
• Experience driving framework adoption and consistent delivery across multiple teams.
• Strong understanding of data quality, lineage, metadata, logging, governance, and auditability.
• Ability to review technical designs and code while asking detailed, practical engineering questions.
• Experience creating technical documentation, onboarding processes, training materials, or implementation playbooks.
• Strong stakeholder‑management and communication skills.
• Ability to translate technical concepts for business and domain users.
• Experience leading or mentoring data engineers or technical resources.
• Ability to combine technical leadership, stakeholder engagement, platform enablement, and hands‑on troubleshooting.
• Ability to work according to the required New York City hybrid schedule.

PREFERRED EXPERIENCE

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