Senior Data Engineer

Dun & Bradstreet

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

Posted 11/12/2026full-timeHyderabad • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing scalable data pipelines and ETL/ELT workflows, with strong proficiency in Python and SQL for large-scale data processing. Capable of contributing to data architecture and delivering production-grade solutions in cloud environments like Google Cloud Platform and AWS.

Highest-signal resume keywords

Data Pipeline DevelopmentPython ProgrammingSQL ExpertiseGoogle Cloud PlatformData Architecture Design

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Hard Skills

Data EngineeringETL/ELT WorkflowsData ModelingQuery OptimizationDistributed SystemsData IngestionData NormalizationData DeduplicationData ValidationData Quality Assurance

Soft Skills

Analytical ThinkingAttention to DetailIndependent Problem SolvingCollaborationAdaptability

Tools & Technologies

PostgresBigQueryRedshiftGoogle Cloud StorageAWS S3DockerKubernetesGitJiraConfluence

Industry Keywords

Data ProcessingLarge-Scale DatasetsCloud ComputingCI/CDAgile Methodologies

Tech Stack

Tools & technologies

Amazon RedshiftAWSBigQueryCloudDistributed SystemsDockerETLGoogle Cloud PlatformKubernetesPostgresPythonSQL

About the role

Key responsibilities & impact

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, including data ingestion, normalization, matching, and deduplication
Process and unify multi-source datasets including cookies, device IDs, behavioral data, third-party data, 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 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

Requirements

What you’ll need

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.)
Strong expertise in 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