Senior Director, Data Platform & Engineering

ZoomInfo is a leading Go-To-Market Intelligence Platform that empowers businesses with AI-ready insights and trusted data. They are seeking a Senior Director of Data Platform & Engineering to lead the Enterprise Data Platform and Engineering functions, overseeing infrastructure, pipelines, and tooling that support various stakeholders. This role involves strategic leadership, platform modernization, and cross-functional partnership to enhance data capabilities across the organization.

Responsibilities:

• Lead and develop two Director-level managers and their respective teams spanning data platform engineering, data pipeline development, and practitioner enablement
• Set the multi-quarter roadmap for the Enterprise Data Platform and Engineering organizations, balancing infrastructure modernization, operational stability, security, and enablement priorities
• Build a high-performance engineering culture grounded in ownership, accountability, and operational excellence
• Drive headcount planning, organizational design, and career development frameworks that attract and retain top talent across a distributed team
• Serve as a trusted partner to the VP of Engineering & Innovation, contributing to broader engineering strategy and organizational decisions
• Establish clear prioritization frameworks to manage competing demands from security, infrastructure modernization, platform stability, and practitioner tooling
• Own the strategic direction for ZoomInfo's core data infrastructure, including Snowflake, Airflow, Fivetran, AWS, and GCP — partnering with the Director of Data Platform on architecture and execution
• Guide the design and buildout of an Iceberg-based GCS Data Lake as the foundation for scalable, cost-efficient storage, including governance, observability, and ingestion patterns
• Oversee cloud infrastructure consolidation to GCP, including Airflow 3.x upgrades, service migrations, and deprecation of legacy environments
• Drive SaaS vendor strategy and cost optimization — evaluating build-vs-buy decisions for ingestion (Fivetran/Airbyte), cost management, and other platform tooling
• Oversee monorepo consolidation and CI/CD standardization to enable consistent governance and accelerate deployment velocity
• Ensure the platform team maintains a strong security posture including service account modernization, key rotation, legacy role decommissioning, access reviews, and incident response readiness
• Oversee the development, reliability, and monitoring of all enterprise data pipelines powering analytics, reporting, and operational workflows — partnering with the Director of Data Engineering on execution and quality
• Guide the evolution of data modeling practices through dbt and semantic models, ensuring data products are trusted, documented, and well-tested
• Champion the shift-left initiative, enabling product and engineering domain teams to own their data assets end-to-end while maintaining quality and standards
• Support the development of AI-assisted tooling (agents and micro apps) that automate data engineering workflows — from ingestion and modeling to deployment and monitoring
• Drive platform-wide data standards alignment for metadata, ownership, testing, and documentation — ensuring remediation of priority assets and retirement of low-value ones
• Oversee the buildout of centralized observability for certified metrics, including compliance monitoring, alerting, and automated routing of quality failures to asset owners
• Serve as the primary data infrastructure and engineering point of contact for leaders across Marketing, Finance, Product, Sales, and HR — translating business needs into platform capabilities
• Partner with cross-functional stakeholders to define metrics, analytics requirements, and data delivery expectations that inform business strategy
• Drive enablement programs — documentation, training, office hours, and onboarding — to accelerate adoption of self-service data tools and standards across the organization
• Represent the data platform and engineering organization in strategic planning conversations, company projects, and technology decisions that have data implications
• Manage vendor relationships and contracts, negotiating strategically to optimize cost, capability, and long-term flexibility

Requirements:

• 10+ years of progressive experience in data engineering, data platform, analytics, or related technical leadership roles, with at least 4 years at the Director level or above
• Experience leading and scaling data organizations of 15+ people, with a track record of building high-performing teams and developing talent
• Strong working knowledge of modern data stack technologies including Snowflake, dbt, Airflow, and cloud platforms (AWS and/or GCP). You do not need to be the deepest technical expert — but you need to understand architecture trade-offs, ask the right questions, and make informed strategic decisions
• Experience driving platform modernization initiatives — whether cloud migrations, tool consolidation, vendor transitions, or infrastructure redesigns
• Demonstrated ability to manage through managers — setting direction, aligning priorities, and holding leaders accountable for outcomes without micromanaging execution
• Proven cross-functional partnership skills with business stakeholders (Marketing, Finance, Sales, Product, HR) — translating ambiguous business needs into structured data solutions
• Experience building self-service analytics capabilities and enablement programs that reduce dependency on central engineering teams
• Strong vendor management experience including contract negotiations, build-vs-buy evaluations, and SaaS cost optimization
• Excellent communication skills — able to present strategy, trade-offs, and progress to executive leadership in both written and verbal formats
• Experience operating in fast-paced, high-growth, or transformation environments where priorities shift and adaptability is essential
• Familiarity with infrastructure-as-code practices (Terraform), CI/CD pipelines, and monorepo strategies
• Experience with data lake architectures (Iceberg, Parquet) and multi-cloud environments
• Exposure to data ingestion frameworks at scale (Fivetran, Airbyte, custom connectors) and reverse ETL patterns
• Familiarity with AI/ML-driven automation for data engineering workflows, including LLM-assisted tooling, agents, or semantic modeling frameworks
• Background in security and compliance within data environments — RBAC, key rotation, masking, HIPAA, or SOC2
• Experience with data cataloging and lineage tools (OpenMetadata, DataHub, or similar)
• Experience with shift-left or data mesh operating models where domain teams own their data end-to-end
• Exposure to BI platform management and modernization (Looker, Tableau, or emerging AI-driven visualization approaches)