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.

ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.

Our mission is simple but ambitious: deliver the most accurate and comprehensive B2B data to power modern go-to-market strategies. We continuously innovate to help businesses identify ideal customers, reach key decision-makers, and accelerate growth through data-driven insights.

The Opportunity

We're looking for a Software Engineer III to join the Analytical Data team - the team that builds and runs the lakehouse for ZoomInfo's Data organization.

ZoomInfo's data about people and companies is produced by many teams and used by many more: analysts, data scientists, product managers, and, increasingly, AI agents. We are building one governed source of that data for all of them. Each dataset is written once as Apache Iceberg tables, kept on both Google Cloud Storage and Amazon S3, and queried in place by BigQuery, Snowflake, and Starburst/Trino - no separate copy or export pipeline per engine. The goal is that anyone in the Data organization, person or agent, can query fresh, trusted data without an engineer in the loop.

This is a hands-on data engineering role. You will work across streaming and batch ingestion, Iceberg table management, multi-engine access, data contracts with producer teams, and the observability that shows the data is fresh and complete. Datasets run to hundreds of millions of records, and most refresh daily.

What You'll Do

Build and run Kotlin and Apache Beam pipelines on Google Cloud Dataflow that load data from Kafka and cloud storage into Apache Iceberg tables.
Keep large Iceberg tables correct, fast, and cost-efficient with good partitioning, Spark maintenance jobs (compaction, deduplication, snapshot expiry, orphan-file cleanup), and reconciliation against the source.
Make each dataset queryable from BigQuery, Snowflake, and Starburst/Trino through Iceberg catalogs and stable consumer views.
Work with producer teams on schemas and data contracts, and keep publishing a new dataset simple and documented.
Build the observability that shows each dataset is fresh, complete, and within budget: Datadog dashboards and monitors today, AI agents for alert triage next.
Own your pipelines in production, from incident response to the fix that keeps the problem from coming back.
Ship through code review, CI/CD, staged releases, and unit, integration, and end-to-end tests, with infrastructure managed in Terraform across GCP and AWS.
Work daily with data consumers, producer teams, and platform teams, and help shape the platform's technical roadmap.

Must-Have Qualifications

Data Engineering and Distributed Systems

3+ years of professional software engineering experience, with a focus on data engineering or distributed systems.
Strong programming skills in Kotlin, Java, or Python. Our ingestion pipelines are written in Kotlin and our table maintenance and orchestration in Python, so you will work in both.
Hands-on experience with Apache Beam (ideally on Google Cloud Dataflow) or Apache Spark.
Experience with Apache Kafka or a similar streaming platform in production pipelines.
Experience with very large datasets and distributed systems, including designing data models and storage layouts that handle large volumes efficiently.

Lakehouse, Query Engines, and Cloud

Understanding of lakehouse architecture and open table formats, ideally Apache Iceberg.
Strong SQL and working knowledge of at least one of BigQuery, Snowflake, or Starburst/Trino.