AWS Data Engineer

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group has over 360,000 team members globally in more than 50 countries. Capgemini specializes in cloud, data, AI, connectivity, software, digital engineering, and platforms.

• Proven experience in Big Data or Cloud projects involving processing and visualization of large and unstructured datasets across various phases of the SDLC.
• Hands-on experience with AWS cloud services including Storage, Compute (and Serverless), Networking, and DevOps.
• Solid understanding of AWS services, ideally supported by relevant certifications.
• Familiarity with technologies such as Glue, Redshift, Lambda, Athena, S3, Snowflake, Docker, Terraform, CloudFormation, Kafka, Airflow, Spark.
• Basic proficiency in at least one programming language: Python, Scala, Java, or Bash.
• Very good command of English; German language skills are an advantage.

Nice to Have:

• Experience with orchestration tools (e.g., Airflow, Prefect).
• Exposure to CI/CD pipelines and DevOps practices.
• Knowledge of streaming technologies (e.g., Kafka, Spark Streaming).
• Experience working with Snowflake or Databricks in production or development.
• Relevant certifications in AWS, data engineering, or big data technologies.

• Design and implement solutions for processing large-scale and unstructured datasets (Data Mesh, Data Lake, or Streaming Architectures).
• Develop, optimize, and test modern DWH/Big Data solutions based on the AWS cloud platform within CI/CD environments.
• Improve data processing efficiency and support migrations from on-premises systems to public cloud platforms.
• Collaborate with data architects and analysts to deliver high-quality cloud-based data solutions.
• Ensure data quality, consistency, and performance across AWS services and environments.
• Participate in code reviews and contribute to technical improvements.