[Hiring] Senior Data Engineer @ZoomInfo

Role Description

The Senior Data Engineer is a core builder of the single source of truth that ZoomInfo's Corporate Strategy and GTM Strategy teams use to plan and track the business: pipeline created, bookings, conversion, renewals, market size, and results against plan. The same certified tables and metric definitions will later feed the Company Brain, the company-wide knowledge layer that employees and AI agents query.

• Build and own end-to-end pipelines in Python and Airflow that ingest Salesforce, marketing, ad platform, conversation intelligence, product usage, billing, and ZoomInfo data into Snowflake.
• Bring plan, quota, headcount, and cost data from Finance, RevOps, and HR systems into the warehouse so Strategy teams can compare results to plan and calculate.
• Establish certified metrics in the semantic layer, each approved by a named business owner, so dashboards, spreadsheets, and AI agents reuse one definition.
• Implement tests, monitoring, documentation, and lineage for uniqueness, completeness, freshness, and reconciliation to Finance, and alert before a stakeholder finds the problem.
• Apply row- and column-level access rules so only cleared people can see sensitive fields such as compensation and quota.
• Run forecasts, territory and capacity models, cohort and retention studies, and impact readouts.
• Partner with Corporate Strategy, GTM Strategy, Business Partners, Finance, RevOps, and central Data Engineering to agree on definitions before building, and turn recurring requests into self-serve certified tables.
• Own pipelines after launch: share the business-hours on-call rotation, find root causes, write short postmortems, and keep runbooks current.
• Follow and contribute to team engineering standards, including version control, code review, CI, and documentation, and help analysts use certified tables and the semantic layer.

Qualifications

• 5+ years in data or analytics engineering, with demonstrated ownership of production pipelines in a cloud warehouse.
• Strong SQL and Python; comfortable writing production-grade code, debugging complex pipelines, and optimizing queries.
• Hands-on experience with a cloud data warehouse (Snowflake strongly preferred; BigQuery or equivalent acceptable).
• Solid dbt experience — has built and maintained models in a production dbt project.
• Experience with orchestration tools — Airflow and Fivetran required; Dagster or equivalent a plus.
• Experience with Databricks and Spark for custom modeling and transformation workloads.
• Experience with GTM data in Salesforce or another CRM, including opportunity history, bookings, and renewals.
• Ability to work autonomously — can take an ambiguous ask, scope it with a stakeholder, and ship a solution with minimal oversight.
• Strong communication skills — can explain technical tradeoffs to non-technical partners and adapt style to different audiences.
• Security-first mindset; familiar with PII handling and access controls.

Preferred Qualifications

• Hands-on experience working with Finance, Marketing, Sales data to support data driven decision.
• Built a semantic layer (dbt Semantic Layer, Cube, LookML, or similar) that more than one tool reads from.
• Prepared data for AI use: documented fields, lineage, access controls, and agent tool access such as MCP servers.
• Built time-series forecasts of pipeline, bookings, or renewals.
• Built territory, capacity, or market-sizing models.
• Used LLMs to classify or extract fields from unstructured text such as call transcripts.
• Data observability tools such as Monte Carlo or Datadog, and AWS or GCP with Terraform.
• Familiarity with ZoomInfo data or similar company and contact data.