Senior GCP Data Engineer (R-19655)

Shape the Future with Dun & Bradstreet

At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. For over 180 years, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers.

Key Responsibilities

Design, build, and optimize scalable data pipelines and ETL/ELT workflows for large, complex datasets.

Design and implement foundational data architecture supporting identity resolution and ID graph systems.

Develop and enhance systems supporting identity resolution and ID graph construction (data ingestion, normalization, matching, and deduplication).

Process and unify multi-source datasets (cookies, device IDs, behavioral data, third-party and proprietary data).

Write efficient, testable, and maintainable code using Python and SQL for large-scale data processing.

Optimize data models, queries, and storage strategies for performance, scalability, and cost efficiency.

Build and maintain data validation, monitoring, and alerting systems to ensure data quality and reliability.

Troubleshoot, debug, and improve existing data pipelines and infrastructure.

Take ownership of complex data problems end-to-end, from initial design through production deployment.

Contribute to key technical decisions related to data architecture, scalability, and system design.

Collaborate with data, platform, DevOps, and product teams to deliver scalable, production-ready solutions.

Translate business and product requirements into practical, performant data solutions.

Document data pipelines, systems, and workflows clearly.

Continuously improve system performance, data quality, and pipeline resilience.

Contribute to building new capabilities that improve how customers understand and leverage data insights.

Key Requirements

5–8+ years of hands-on experience in data engineering or large-scale data processing.

Proven experience building and maintaining production-grade data pipelines and distributed systems.

Experience contributing to the architecture and delivery of large-scale data platforms or mission-critical data systems.

Strong expertise in

SQL and relational databases (Postgres, BigQuery, Redshift, etc.)

Python for data processing and analysis

Experience with Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Functions) and/or AWS (S3, Redshift, EMR, RDS).

Experience working with large-scale datasets (hundreds of millions to billions of records).

Strong understanding of data modeling, partitioning, indexing, and query optimization.

Experience with distributed data processing and parallelization techniques.

Experience moving large volumes of data across systems and architectures.

Familiarity with CI/CD, containerization, and orchestration tools (Docker, Kubernetes, GitHub Actions, etc.).

Strong debugging and troubleshooting skills in complex data environments.

Experience with version control (Git) and Agile tools (Jira, Confluence, etc.).

Highly analytical with strong attention to detail and a data-driven mindset.

Ability to hit the ground running, quickly understand systems, and deliver independently.

Comfortable working in a remote, fast-paced, and collaborative environment.

Strong ability to contribute to system design and deliver implementations independently.