Vice President, Data Platform Engineering

• Drive modernization from legacy and on-prem systems to modern, cloud-native, and hybrid data platforms
• Architect and lead the development of a Multi-Agent ETL Platform for batch and event streaming, integrating AI agents to autonomously manage ETL tasks such as data discovery, schema mapping, and error resolution
• Define and implement data ingestion, transformation, and delivery pipelines using scalable frameworks (e.g., Apache Airflow, Nifi, dbt, Spark, Kafka, or Dagster)
• Leverage LLMs, and agent frameworks (e.g., LangChain, CrewAI, AutoGen) to automate pipeline management and monitoring
• Ensure robust data governance, cataloging, versioning, and lineage tracking across the ETL platform
• Define project roadmaps, KPIs, and performance metrics for platform efficiency and data reliability
• Establish and enforce best practices in data quality, CI/CD for data pipelines, and observability
• Collaborate closely with cross-functional teams (Data Science, Analytics, and Application Development) to understand requirements and deliver efficient data ingestion and processing workflows
• Establish and enforce best practices, automation standards, and monitoring frameworks to ensure the platform’s reliability, scalability, and security
• Build relationships and communicate effectively with internal and external stakeholders, including senior executives, to influence data-driven strategies and decisions
• Continuously engage and improve teams’ performance by conducting recurring meetings, knowing your people, managing career development, and understanding who is at risk
• Oversee deployment, monitoring, and scaling of ETL and agent workloads across multi cloud environments
• Continuously improve platform performance, cost efficiency, and automation maturity

• Hands-on experience in data engineering, data platform strategy, or a related technical domain
• Proven experience leading global data engineering or platform engineering teams
• Proven experience in building and modernizing distributed data platforms using technologies such as Apache Spark, Kafka, Flink, NiFi, and Cloudera/Hadoop
• Strong experience with one or more of data pipeline tools (Nifi, Airflow, dbt, Spark, Kafka, Dagster, etc.) and distributed data processing at scale
• Proficiency in Python, SQL, and data ecosystems (Oracle, AWS Glue, Azure Data Factory, BigQuery, Snowflake, etc.)
• Deep understanding of data modeling, metadata management, and data governance principles
• Proven success in leading technical teams and managing complex, cross-functional projects
• Excellent communication skills, with the ability to tailor technical concepts to executive, operational, and technical audiences
• Expertise and ability to lead technical decision-making considering scalability, cost efficiency, stakeholder priorities, and time to market
• Proven track leading high-performing teams with experience leading and coaching director level reports and experienced individual contributors
• Bachelor’s degree in Data Science, Computer Science, Information Technology, Business Administration, or a related field. Equivalent experience will also be considered
• Must be eligible to work in the United States, now as well as in the future, without employer sponsorship

Experience building and managing AI-augmented or agent-driven systems will be a plus

• Insurance (including medical, prescription drug, dental, vision, disability, life insurance)
• flexible spending account and health savings account
• paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave)
• 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire
• 10 annual paid U.S. observed holidays
• 401k with a best-in-class company match
• deferred compensation for eligible roles
• fitness reimbursement or on-site fitness facilities
• eligibility for tuition reimbursement