Lead Data Engineer / Databricks
Confirmed on the employer's own hiring board on Oct 5, 2026. First seen by Alion on Sep 17, 2026. Capgemini scores B on the Alion truth index.
Capgemini
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Your Role
- Design and implement enterprise-scale data ingestion frameworks using Databricks Native Data Loader, Lakeflow Connect, and other Databricks-native capabilities.
- Build and optimize batch and streaming data pipelines for ingesting data from Oracle, MySQL, SQL Server, APIs, and other enterprise data sources.
- Develop robust ELT/ETL pipelines leveraging Databricks, Spark, Delta Lake, and cloud-native technologies.
- Implement end-to-end data movement, transformation, validation, and monitoring solutions.
- Lead architectural decisions related to data ingestion, storage, processing, and governance.
- Provide technical leadership and mentoring to junior engineers.
- Collaborate with Product, Engineering, Analytics, Architecture, and Business stakeholders to understand requirements and deliver data solutions.
- Drive best practices in coding, DevOps, CI/CD, testing, and documentation.
- Participate in architecture reviews, code reviews, and technical design discussions.
- Support Agile development processes and contribute to sprint planning and estimation activities
Your Profile
- We are seeking a highly skilled Lead Data Engineer to design, develop, and optimize scalable data ingestion and processing solutions using Databricks native capabilities. The ideal candidate will possess deep expertise in data engineering, large-scale data integration, and cloud-based data platforms, with hands-on experience in Databricks Native Data Loader, Lakeflow Connect, Delta Lake, and enterprise data migration from relational databases such as Oracle, MySQL, and SQL Server.
- This role will lead the design and implementation of modern data pipelines, mentor engineering teams, and collaborate closely with business and technology stakeholders to deliver reliable, scalable, and high-performing data solutions.
What you’ll love
Empowered Careers with Purpose: Work on meaningful projects that use technology to solve real-world challenges.
Growth and Learning at Every Step: Access continuous learning through internal academies, certifications and mentorship.
Own your growth: Open access to digital learning platforms
About Capgemini
Capgemini (Footer)
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of
over 420,000 team members in more than 50 countries. We deliver end-toend services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations.
At Capgemini Mexico, we aim to attract the best talent and are committed to creating a diverse and inclusive work environment, so there is no discrimination based on race, sex, sexual orientation, gender identity or expression, or any other characteristic of a person. All applications welcome
and will be considered based on merit against the job and/or experience for the position.
Job Description
Data engineers are responsible for building reliable and scalable data infrastructure that enables organizations to derive meaningful insights, make data-driven decisions, and unlock the value of their data assets.
Job Description - Grade Specific
The involves leading and managing a team of data engineers, overseeing data engineering projects, ensuring technical excellence, and fostering collaboration with stakeholders. They play a critical role in driving the success of data engineering initiatives and ensuring the delivery of reliable and high quality data solutions to support the organizations data driven objectives.