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ZoomInfo Technologies LLC

Software Engineer III - PA085

Waltham, Massachusetts, United StatesFrom $176kmidAdded yesterday

About this role

ZoomInfo seeks a Software Engineer III for its Data Acquisition team to design and build large-scale backend data pipelines that ingest, validate, and store raw data from multiple sources. You'll work with modern data technologies like Apache Airflow, Spark, and Kafka while maintaining production-quality systems on cloud infrastructure.

What you'll do

  • Design and build backend data pipelines for ingesting, validating, normalizing, and storing high-volume raw data
  • Develop ETL/ELT workflows and processing jobs using Apache Airflow, Apache Beam, Google Dataflow, Spark, and Kafka
  • Implement new features in Java-based services and data processing applications at scale
  • Improve data quality through schema validation, deduplication, enrichment, and monitoring
  • Contribute to pipeline observability for health, throughput, latency, cost, and error rates
  • Collaborate with product and platform teams to translate business requirements into reliable technical solutions

What they're looking for

  • Java and object-oriented programming
  • Data pipeline design and ETL/ELT workflows
  • Apache Beam, Apache Airflow, Spark, Google Dataflow, DataProc
  • Streaming technologies (Kafka, Google Pub/Sub)
  • Google Cloud Platform (GCP), BigQuery, GCS, GKE
  • SQL and large-scale data storage systems
  • Batch and streaming processing architectures
  • Production-level observability and troubleshooting
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ZoomInfo Technologies LLC

ZoomInfo Technologies LLC builds advanced data infrastructure and technographic data services, leveraging cloud platforms and AI to deliver intelligence applications. The company is hiring DevOps engineers, software engineers, and data specialists to design scalable data pipelines, manage cloud infrastructure, and develop customized solutions for enterprise clients.

Website
zoominfo.com
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Likely interview questions

  • Describe a production data pipeline you built—what sources did it ingest, and how did you handle scale and reliability?
  • How do you approach debugging a data pipeline failure in production, and what tools or metrics would you use?
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