Principal Data Engineer

Dun & Bradstreet India · India

Key Responsibilities:

• 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
• Own and drive complex data problems end-to-end, from initial design through production deployment
• Make and influence 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:

• 8-12+ 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
• Demonstrated experience architecting and delivering large-scale data platforms or mission critical data systems
• Strong expertise in: o 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
• Proven ability to drive system design and implementation.