Senior Software Engineer - Data Acquisition Team

ZoomInfo Technologies LLC•Waltham, MA

7d•$140,000 - $220,000•Hybrid

About The Position

We're looking for a Senior Software Engineer to join the Data Acquisition team - one of ZoomInfo's most strategically important engineering areas. In this role, you will design, build, and operate the backend systems and data pipelines that acquire, transform, validate, and store large raw data sets from a wide range of sources. You will work across distributed processing, workflow orchestration, streaming and batch data flows, and cloud infrastructure. This is a senior individual-contributor engineering role focused on technical depth, production execution, and high-quality data systems. The right candidate brings strong backend engineering fundamentals, significant pipeline experience, and the ability to turn complex data acquisition requirements into scalable systems.

Requirements

5+ years of professional software engineering experience with a strong focus on backend systems, data engineering, or distributed processing.
Proven experience building and operating production data pipelines at scale.
Deep proficiency with Java and object-oriented design.
Hands-on expertise with data processing and orchestration technologies such as Apache Beam, Apache Airflow, Spark, Google Dataflow, or DataProc.
Strong experience with streaming systems such as Apache Kafka, Google Pub/Sub, or similar technologies.
Strong understanding of batch processing, streaming processing, data modeling, schema evolution, and data quality management.
Experience designing ETL/ELT workflows that process large volumes of structured and semi-structured data.
Experience designing high-throughput, fault-tolerant backend services and distributed systems.
Strong understanding of APIs, integration patterns, retries, backpressure, idempotency, and operational failure modes.
Ability to write clean, maintainable production code and evaluate tradeoffs in system design.
Experience with large-scale storage and query technologies such as BigQuery, Snowflake, Trino, or similar systems.
Experience with at least one cloud provider, preferably GCP.
Hands-on experience with cloud services such as BigQuery, GCS, GKE, Dataflow, DataProc, and Pub/Sub.
Experience operating production services with monitoring, logging, alerting, SLIs, and incident investigation.
Ability to analyze and improve pipeline performance, reliability, resource utilization, and cost.
Bachelor's degree in Computer Science, Software Engineering, or a related field.
Ability to translate business and data requirements into clear technical designs and implementation plans.
Pragmatic engineering judgment with the ability to balance correctness, delivery speed, maintainability, and operational risk.

Nice To Haves

Experience with Kubernetes, especially GKE or EKS, for running distributed workloads.
Experience with Terraform or other infrastructure-as-code tools.
Experience with Snowflake, BigQuery, Starburst/Trino, or similar query engines.
Knowledge of data integration patterns involving CRM systems, email/calendar APIs, third-party feeds, change data capture, or external data providers.
Experience in a B2B data company, data marketplace, or data-as-a-product environment.
Expert-level experience with Apache Spark or another distributed processing framework.

Responsibilities

Design, build, and operate large-scale data acquisition pipelines that ingest, validate, transform, enrich, and store high-volume raw data.
Architect resilient ETL/ELT workflows for batch, streaming, scheduled, and event-driven data processing.
Develop production Java services and data processing applications for ingestion, orchestration, enrichment, deduplication, and delivery.
Build and improve systems using technologies such as Apache Airflow, Apache Beam, Spark, Google Dataflow, DataProc, Kafka, and Pub/Sub.
Define practical approaches for schema evolution, data contracts, data validation, backfills, replayability, and idempotent processing.
Improve reliability, performance, scalability, and cost efficiency across data acquisition pipelines and services.
Implement observability, monitoring, and alerting for pipeline health, throughput, latency, failure rates, and data quality metrics.
Work with product, data science, platform, and data quality teams to translate business needs into production-ready systems.
Contribute to technical designs, implementation plans, and system modernization efforts across Data Acquisition.