{"id":2869530,"url":"https://alion.io/job/zoominfo-software-engineer-iii-analytical-data","title":"Software Engineer III - Analytical Data","company":{"id":3180,"name":"ZoomInfo","domain":"zoominfo.com","url":"https://alion.io/company/zoominfo","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":76,"open_postings":29,"ghost_share":0,"stale_share":0.966,"repost_share":0,"time_to_fill_p50_days":43,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Backend","role_family":"Backend","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Washington, United States","Waltham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":112000,"max":176000,"currency":"USD","period":"year","gross":null,"usd_annual":176000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Datadog","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Java","optional":false},{"name":"Kotlin","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Trino","optional":false},{"name":"Airflow","optional":true},{"name":"AWS Glue","optional":true},{"name":"dbt","optional":true}],"status":"live","first_seen_at":"2026-10-19T18:08:55Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-20T06:11:44Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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.\nZoomInfo (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.\nOur 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.\nThe Opportunity\nWe'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.\nZoomInfo'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.\nThis 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.\nWhat You'll Do\nBuild and run Kotlin and Apache Beam pipelines on Google Cloud Dataflow that load data from Kafka and cloud storage into Apache Iceberg tables.\nKeep 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.\nMake each dataset queryable from BigQuery, Snowflake, and Starburst/Trino through Iceberg catalogs and stable consumer views.\nWork with producer teams on schemas and data contracts, and keep publishing a new dataset simple and documented.\nBuild the observability that shows each dataset is fresh, complete, and within budget: Datadog dashboards and monitors today, AI agents for alert triage next.\nOwn your pipelines in production, from incident response to the fix that keeps the problem from coming back.\nShip through code review, CI/CD, staged releases, and unit, integration, and end-to-end tests, with infrastructure managed in Terraform across GCP and AWS.\nWork daily with data consumers, producer teams, and platform teams, and help shape the platform's technical roadmap.\nMust-Have Qualifications\nData Engineering and Distributed Systems\n3+ years of professional software engineering experience, with a focus on data engineering or distributed systems.\nStrong 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.\nHands-on experience with Apache Beam (ideally on Google Cloud Dataflow) or Apache Spark.\nExperience with Apache Kafka or a similar streaming platform in production pipelines.\nExperience with very large datasets and distributed systems, including designing data models and storage layouts that handle large volumes efficiently.\nLakehouse, Query Engines, and Cloud\nUnderstanding of lakehouse architecture and open table formats, ideally Apache Iceberg.\nStrong SQL and working knowledge of at least one of BigQuery, Snowflake, or Starburst/Trino.\nExperience with Protobuf or Avro schemas and evolving them without breaking downstream consumers.\nExperience with GCP or AWS. We are GCP-first, with storage and catalogs on AWS as well.\nEngineering Practices and Operations\nGood understanding of the SDLC for data pipelines, including unit, integration, and end-to-end tests.\nExperience running pipelines in production with metrics, dashboards, and alerts (for example in Datadog), and a strong ownership mindset: you take responsibility for production incidents end-to-end.\nPractical, everyday use of AI coding assistants such as Claude Code or Codex, with the judgment to review and test what they produce.\nCollaboration and General\nExperience in cross-team collaboration: working directly with producer teams on schema changes and daily with data consumers (analytics, ML) and platform/infrastructure teams, with the ability to navigate different contexts and technical vocabularies.\nBachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.\nPragmatic engineering judgment with the ability to balance quality, speed, and business impact.\nNice to Have\nProduction experience with Apache Iceberg: table maintenance, partition and schema evolution, and Iceberg catalogs such as BigLake metastore (now Google's Lakehouse runtime catalog), AWS Glue, or Project Nessie.\nExperience with metadata catalogs and data governance tooling such as OpenMetadata.\nExperience building AI agents that watch pipelines, triage alerts, or investigate data issues.\nExperience serving the same data to several query engines, or running data infrastructure across GCP and AWS.\nExperience negotiating data contracts between producer and consumer teams.\nExperience contributing to technical strategy or a roadmap for data processing infrastructure.\nExperience with Terraform, Apache Airflow (Cloud Composer), or dbt.\nWhy This Role Matters\nWithout a shared layer, each dataset gets copied into each warehouse, and every copy brings its own export pipeline, access process, and definition. Data becomes slow to reach, expensive to keep, and easy to disagree about. The lakehouse replaces those copies with one governed source that every team and every engine reads.\nYou will join a small team early in this work, with ownership of production systems and a direct say in the architecture, in the standards producer teams follow, and in how the Data organization uses its data.\nActual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\nIn addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here.\nBelow is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n$112,000—$176,000 USD\nAbout us: \nZoomInfo (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.\nZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\nZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here.\nZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status. We welcome all applicants and are committed to providing equal employment opportunities regardless of sex, race, age, color, national origin, sexual orientation, gender identity, marital status, disability status, religion, protected military or veteran status, medical condition, or any other characteristic protected by applicable law. We also consider qualified candidates with criminal histories in accordance with legal requirements.\nFor Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. 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