Senior Data Engineer

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.

What you will do:

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.

What you bring:

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

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