Senior Data Engineer

ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.   We are looking for a highly skilled Senior Data Engineer to become part of our core Enterprise Data Engineering team. 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. The ideal candidate has a strong background in big data processing, pipeline orchestration, and data modeling, with a proven track record of delivering scalable and high-quality data solutions in fast-paced, data-centric product environments. Given the dynamic nature of emerging technologies, this role requires an individual who excels at exploration and embraces continuous learning as core responsibilities. You'll constantly research and implement innovative solutions while integrating vast, diverse data sources into our AI applications, including our industry-leading LLM-powered systems. This role also carries production ownership: you will share on-call responsibility for the pipelines and platforms you build, and you're comfortable triaging, escalating, and driving incidents to resolution under pressure.   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, ensuring high-quality and reliable ingestion. 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 that directly enhance customer value. Define, monitor, and enforce data quality SLAs across all pipelines and products, ensuring data accuracy and lineage are a top priority. Participate in a shared PagerDuty on-call rotation, responding to pipeline and platform incidents, performing root-cause analysis, and driving remediation and postmortems. Triage production issues quickly and escalate appropriately, knowing when to loop in engineering leadership, Platform engineers, adjacent teams, or business stakeholders based on severity, blast radius, and customer impact. Operate effectively amid ambiguity by making sound judgment calls and iterating with stakeholders rather than waiting for perfect clarity. Mentor and coach junior engineers, promoting best practices in code quality, data architecture, incident response, and operational excellence. Participate in architectural decisions and long-term strategy planning for our enterprise-wide data infrastructure, with a focus on cost, performance, reliability, and observability. Contribute to and maintain runbooks, on-call documentation, and operational playbooks to reduce time-to-resolution for future incidents.   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 in a modern data stack. 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) for proactive detection of data quality and pipeline issues. Familiarity with CI/CD practices applied to data workflows (e.g., automated testing for pipelines, version-controlled data models).   Required Non-Technical Skills Excellent communication skills – ability to explain complex technical concepts to both engineering teams and non-technical stakeholders, especially during high-pressure incidents. Strategic & Product-Ori