Senior Data Engineer / Analytics Engineer

Benefits and Life at Capgemini

This role is based in Chicago, IL with a hybrid work arrangement. Compensation ranges from USD 110,841 to 145,000 per year, reflecting a strong commitment to rewarding capability and impact. Capgemini provides a comprehensive benefits package designed to support you and your family across work, health, finances, and personal growth.

• Flexible work arrangements to support work-life balance
• Healthcare coverage including dental, vision, and mental health resources
• Financial well being programs such as 401(k) and an Employee Stock Ownership Plan
• Paid time off and paid holidays
• Paid parental leave
• Family building benefits including adoption assistance, surrogacy, and cryopreservation
• Social well being benefits like subsidized back-up child and elder care, plus tutoring support
• Mentoring, coaching, and continuing learning programs
• Employee Resource Groups to connect and grow
• Disaster relief support when needed

About the Role

Capgemini is seeking a highly skilled data professional to join the Analytics Engineering team within the Service Analytics and AI organization. The role centers on designing, building, and sustaining scalable data pipelines and analytics solutions that empower advanced analytics, business intelligence, and data science initiatives. A key focus is constructing a semantic data layer and delivering data products that enable AI driven insights and improved customer experiences.

Responsibilities

• Lead the design, development, and deployment of scalable data pipelines, ensuring robust data integration across diverse systems
• Establish and uphold analytics engineering best practices, including coding standards, governance, performance optimization, and automation
• Participate in code reviews, provide constructive feedback, and contribute to continuous improvement of coding practices
• Design, build, and maintain ETL and ELT pipelines, reusable frameworks, and libraries to process data from multiple sources into a data warehouse
• Proactively monitor and troubleshoot data pipelines to maintain high availability and performance
• Implement CI/CD pipelines to streamline deployment, testing, and maintenance of analytics processes
• Collaborate with data scientists, engineers, analysts, product managers, and business stakeholders to translate requirements into technical specifications
• Communicate complex technical concepts to non-technical stakeholders to ensure alignment across teams

Requirements

• Hands-on experience with SQL, Python, dbt, and Snowflake
• Proficiency with Git and workflow management tools such as Airflow
• Proven track record in designing and building scalable data pipelines and architectures
• Strong understanding of data governance, quality assurance, and performance optimization in data engineering
• Expertise in ETL/ELT, data modeling, and integrating data from multiple sources into a data warehouse
• Experience with CI/CD workflows and related tools for data engineering
• Strong problem-solving and analytical abilities, with collaboration in cross-functional settings

Technologies

• SQL
• Python
• dbt
• Snowflake
• Git
• Airflow