Databricks Senior Data Engineer

Capgemini is an AI-powered global business and technology transformation partner with nearly 60 years of heritage, employing over 420,000 team members in more than 50 countries. The Insights & Data team consists of over 400 professionals delivering advanced data-driven solutions, specializing in Cloud & Big Data engineering across AWS, Azure, and GCP, managing the full Software Development Life Cycle (SDLC) with modern data frameworks and DevOps practices.

• Strong experience in data engineering, with at least 1 year of hands-on Databricks expertise.
• Strong experience in Python for automation and data transformation.
• Experience working with at least one major cloud platform (AWS, Azure, or GCP).
• Strong communication skills and strong English language skills.

Nice to have:

• Solid understanding of SQL with experience in query optimization and data modeling.
• Familiarity with DevOps methodologies, CI/CD pipelines, and Infrastructure as Code (Terraform, Bicep).
• Experience with real-time data streaming technologies such as Kafka or Spark Streaming.
• Knowledge of cloud storage solutions like Data Lake, Snowflake, or Synapse.
• Hands-on experience with PySpark for distributed data processing.
• Relevant certifications such as Databricks Certified Data Engineer Associate or cloud-based data certifications.

• Design and implement data processing solutions using Databricks for large-scale and diverse datasets.
• Design, build, and enhance data pipelines with Python and cloud-native tools.
• Work closely with solution architects to define and uphold best practices in data engineering.
• Ensure data consistency, security, and scalability within cloud-based environments.

Capgemini is committed to diversity and inclusion, ensuring fairness in all employment practices by evaluating individuals based on qualifications and performance, not personal characteristics.