About ZoomInfo
ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life.
What you'll do
- Design, develop, and maintain high-performance, product-centric data pipelines using Airflow, DBT, and Python
- Architect and optimize the massive-scale data warehouse and lakehouse that serves as our single source of truth for all customer data, primarily using Snowflake
- Lead the integration of diverse structured and unstructured data sources (e.g., web data, third-party APIs) into our data ecosystem
- Define roadmap priorities that anticipate internal consumer needs and drive competitive advantage in data and AI capabilities
- Serve as a trusted advisor to leadership on strategy, AI-readiness, and data infrastructure investment decisions
- Collaborate with ML engineers, data scientists, and product managers to translate business needs into scalable data solutions
- Define, monitor, and enforce data quality SLAs across all pipelines and products
- Participate in a shared PagerDuty on-call rotation, responding to pipeline and platform incidents
- Triage production issues quickly and escalate appropriately
- Operate effectively amid ambiguity by making sound judgment calls
- Mentor and coach junior engineers, promoting best practices
- Participate in architectural decisions and long-term strategy planning
- Contribute to and maintain runbooks, on-call documentation, and operational playbooks
What you bring
- Expert-level SQL for building performant, scalable queries and transformations on massive datasets
- Strong Python programming skills with a focus on distributed computing, data manipulation, and building robust APIs
- Production-level experience for large-scale batch and streaming data processing
- Hands-on experience with DBT (Data Build Tool) for advanced data modeling and transformations
- Deep knowledge of Snowflake data warehouse design, optimization, and cost modeling
- Experience owning production systems, including on-call rotations (e.g., PagerDuty, Opsgenie), incident response, and postmortem processes
- Strong understanding of data architecture concepts, including data lakes, event-driven architectures (e.g., Kafka), ETL/ELT, and data mesh
- Proficiency with cloud platforms (GCP and/or AWS) and infrastructure as code (e.g., Terraform)
- Experience with monitoring/observability tooling (e.g., Datadog, Monte Carlo, Grafana)
- Familiarity with CI/CD practices applied to data workflows
Required Non-Technical Skills
- Excellent communication skills – ability to explain complex technical concepts to both engineering teams and non-technical stakeholders
- Strategic & Product-Oriented Thinking – can translate business objectives and customer needs into scalable, high-impact data solutions
- Leadership & Mentorship – experience guiding and uplifting engineering teams
- Stakeholder Management – able to collaborate effectively across departments
- Sound Judgment Under Ambiguity – comfortable making decisions with incomplete information
- Ownership & Accountability – takes responsibility for the full lifecycle of what you build
- Strong documentation habits and ability to evangelize best practices