Principal Engineer (R-18436)

Why We Work at Dun & Bradstreet

Dun & Bradstreet unlocks the power of data through analytics, creating a better tomorrow. Each day, we are finding new ways to strengthen our award-winning culture and accelerate creativity, innovation and growth. Our 6,000+ global team members are passionate about what we do. We are dedicated to helping clients turn uncertainty into confidence, risk into opportunity and potential into prosperity. Bold and diverse thinkers are always welcome. Come join us! Learn more at dnb.com/careers.

Our global community of colleagues bring a diverse range of experiences and perspectives to our work. You'll find us working from a corporate office or plugging in from a home desk, listening to our customers and collaborating on solutions. Our products and solutions are vital to businesses of every size, scope and industry. And at the heart of our work, you’ll find our core values: to be data inspired, relentlessly curious and inherently generous. Our values are the constant touchstone of our community; they guide our behavior and anchor our decisions.

Key Responsibilities:

• Design and Develop Data Pipelines: Architect, build, and deploy scalable and efficient data pipelines within our Big Data ecosystem using Apache Spark and Apache Airflow. Document new and existing pipelines and datasets to ensure clarity and maintainability.
• Data Architecture and Management: Demonstrate familiarity with data pipelines, data lakes, and modern data warehousing practices, including virtual data warehouses and push-down analytics. Design and implement distributed data processing solutions using technologies like Apache Spark and Hadoop.
• Programming and Scripting: Exhibit expert-level programming skills in Python, with the ability to write clean, efficient, and maintainable code.
• Cloud Infrastructure: Utilize cloud-based infrastructures (AWS/GCP) and their various services, including compute resources, databases, and data warehouses. Manage and optimize cloud-based data infrastructure, ensuring efficient data storage and retrieval.
• Workflow Orchestration: Develop and manage workflows using Apache Airflow for scheduling and orchestrating data processing jobs. Create and maintain Apache Airflow DAGs for workflow orchestration.
• Big Data Architecture: Possess strong knowledge of Big Data architecture, including cluster installation, configuration, monitoring, security, resource management, maintenance, and performance tuning.
• Innovation and Optimization: Create detailed designs and proof-of-concepts (POCs) to enable new workloads and technical capabilities on the platform. Collaborate with platform and infrastructure engineers to implement these capabilities in production. Manage workloads and optimize resource allocation and scheduling across multiple tenants to fulfill service level agreements (SLAs).
• Continuous Learning and Collaboration: Participate in planning activities and collaborate with data science teams to enhance platform skills and capabilities.

Key Skills:

• Minimum 10+ years of hands-on experience in Big Data technologies, including a minimum of 3 year's experience working with Spark, Pyspark.
• Experience with Google Cloud Platform (GCP) is preferred, particularly with Dataproc, and at least 6 years of experience in cloud environments is required.
• Must have hands-on experience in managing cloud-deployed solutions, preferably on AWS, along with NoSQL and Graph databases.
• Prior experience working in a global organization and within a DevOps model is considered a strong plus.