Senior AWS Data Engineer: Scalable Data Pipelines

Capgemini; Wrocław; Hybrid; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Yearly financial bonus

Private medical care with Medicover

Life insurance

NAIS benefits platform

Access to 70+ training tracks

Hybrid work model

Job summary

Capgemini is seeking a Data Engineer in Wroclaw to design and maintain scalable data pipelines within the AWS ecosystem. You will collaborate with Architects and engineering teams to deliver reliable data solutions and ensure quality and governance across cloud environments.

You will work with Python for ETL/ELT, handle large datasets, and contribute to documentation and best practice improvements in a hybrid office setup.

Qualifications

• Experience delivering complex data engineering tasks independently.
• Hands-on with cloud-based data pipelines.
• Knowledge of AWS Data & Analytics services.
• Strong Python skills for ETL/ELT processing.
• Ability to work with large datasets and optimize performance.
• Experience with Azure or GCP is a plus.

Responsibilities

• Design, develop, and maintain data pipelines and ETL/ELT processes in AWS.
• Use AWS services like S3, Glue, Lambda, EMR, Redshift, Athena.
• Build distributed data processing workflows for large-scale ingestion.
• Collaborate with architects and engineers to ensure scalable, secure data solutions.
• Ensure data quality, governance, and compliance across cloud environments.
• Participate in code reviews and improve engineering standards.
• Create and maintain technical documentation on pipelines and architecture.

Skills

Data engineering

Cloud pipelines

AWS Analytics

Python ETL

Big data

Data modeling

Azure or GCP

Job description

Capgemini is seeking a Data Engineer in Wroclaw to design and maintain scalable data pipelines within the AWS ecosystem. You will collaborate with Architects and engineering teams to deliver reliable data solutions and ensure quality and governance across cloud environments.

You will work with Python for ETL/ELT, handle large datasets, and contribute to documentation and best practice improvements in a hybrid office setup.