Analytics Engineer, Service Ops Analytics & AI at Capgemini · New York

The goal of analytics engineering team within the Service Analytics and AI organization is to build curated data products leveraging data from structured and unstructured enterprise data sources to enable business intelligence, data science, and advanced analytics.

Seeking a highly skilled and motivated data engineer to join Analytics Engineering team within the Service Analytics and AI organization. This role is pivotal in designing, building, and maintaining scalable data pipelines and analytics solutions that empower Advanced Analytics, Business Intelligence, and Data Science initiatives. You will play a crucial role in building a semantic data layer, defining and implementing cutting-edge data products, and delivering innovative AI-driven solutions that fuel business growth and enhance customer experience.

Key Responsibilities

• Data Pipeline Development: Lead the design, development, and deployment of scalable and robust data pipelines, ensuring seamless data integration and processing across diverse systems.
• Analytics Engineering Best Practices: Establish and uphold best practices for data engineering, including coding standards, data governance, performance optimization, and automation strategies.
• Code Quality and Review: Participate in code reviews, provide constructive feedback, and contribute to the team's continuous improvement in coding practices and methodologies.
• ETL/ELT Development: Design, build, and maintain robust ETL/ELT pipelines, reusable frameworks, and libraries to process and transform data from diverse sources, ensuring accuracy, quality, and consistency.
• System Monitoring: Proactively monitor and troubleshoot data pipelines, ensuring high availability, reliability, and performance across all data engineering workflows.
• Automation and CI/CD: Implement CI/CD pipelines to streamline the deployment, testing, and maintenance of analytics engineering processes.
• Cross-functional Collaboration: Partner with data scientists, engineers, analysts, product managers, and business stakeholders to understand requirements, translate them into actionable technical specifications, and deliver impactful data solutions.
• Stakeholder Communication: Articulate complex technical concepts to non-technical stakeholders, fostering alignment and ensuring a shared understanding of data initiatives across teams.

Qualifications

• Hands-on experience with SQL, Python, dbt, and Snowflake.
• Experience in version control systems such as Git, and workflow management tools such as Airflow
• Proven experience in designing and building scalable data pipelines, and architectures.
• Strong understanding of data governance, quality assurance, and performance optimization in a data engineering context.
• Expertise in ETL/ELT processes, data modeling, and integration of data from multiple sources into a data warehouse.
• Experience with CI/CD workflows and tools for data engineering.
• Strong problem-solving and analytical skills, with the ability to work effectively in a collaborative environment.

If you are passionate about data engineering and ready to take on a challenging, impactful role, we encourage you to apply. Join us in building the next-gen data ecosystem that powers the future of Insurance Service and Customer Analytics!