Data Platform Manager, ZoomInfo

• 8+ years of experience in data engineering, data platforms, or data architecture
• 2+ years of experience managing, mentoring, or leading data engineering teams
• Demonstrated experience designing and operating production-grade data platforms and cloud architectures
• Strong experience with data orchestration, transformation frameworks, and pipeline development
• Strong understanding of data security, access controls, governance, and data contracts
• Proven ability to balance hands-on technical contribution with people leadership
• Excellent written and verbal communication skills

Desirable

• We are seeking an experienced Manager, Data Platform to lead the strategy, architecture, and execution of our modern data platform supporting analytics, machine learning, operational systems, and AI-enabled applications
• You will be a senior member of the larger AI, Data and Innovation organization, responsible for designing and expanding enterprise-level data infrastructure that enables ZoomInfo's internal teams to interact with data comprehensively
• Our core tech stack includes Databricks, Snowflake, AWS, Spark, dbt, Fivetran, Airflow, and BigQuery
• This role combines hands-on technical leadership with people management
• You will manage, mentor, and develop a team of data platform engineers while remaining deeply engaged in architecture, technical direction, platform reliability, and cross-functional planning
• The ideal candidate is a proven team builder who can translate business and stakeholder needs into scalable technical solutions across data engineering, cloud infrastructure, orchestration, governance, and observability
• Define and evolve the vision, roadmap, and technical strategy for the data platform
• Own architectural decisions across ingestion, transformation, orchestration, storage, serving, governance, and observability
• Establish engineering standards and patterns for pipelines, data contracts, infrastructure, and deployment
• Evaluate technologies, vendors, and architectural approaches in partnership with stakeholders
• Remain hands-on in critical technical initiatives, architecture reviews, prototyping, and complex troubleshooting
• Manage, mentor, and develop a team of data platform engineers
• Set clear expectations around technical execution, ownership, collaboration, and operational excellence
• Support career growth through regular feedback, coaching, and performance management
• Participate in hiring, onboarding, interviewing, and team capacity planning
• Partner with leadership to define team structure, priorities, and long-term capability needs
• Oversee the development and maintenance of reliable, scalable data pipelines and workflows
• Establish service-level objectives (SLOs), data quality standards, monitoring, alerting, and incident response practices
• Drive platform migrations and modernization efforts with a focus on incremental delivery and risk reduction
• Reduce operational toil through automation, self-service capabilities, and standardized infrastructure
• Establish appropriate access controls, data protection mechanisms, and security practices for sensitive data
• Define and promote data contracts and clear ownership boundaries between source systems, data platforms, and downstream consumers
• Improve data discoverability, lineage, and trust across the organization
• Ensure platform architecture supports secure access for analytics, machine learning, and AI workloads
• Partner with application engineering, analytics engineering, data science, product, security, and infrastructure teams
• Translate stakeholder needs into clear technical requirements and prioritized initiatives
• Communicate architectural decisions, tradeoffs, and progress to both technical and non-technical audiences