Data Engineer developing sustainable data assets for machine learning and analytics solutions. Collaborating with teams and using modern technologies in a hybrid work setting.
About the role
- Junior Data Engineer developing scalable data pipelines and ETL processes for Capgemini. Collaborating with data scientists and analysts to maintain data integrity and compliance.
Responsibilities
- Design, develop, and maintain scalable data pipelines and ETL processes using Databricks
- Design and develop Python scripts for data transformation, automation, and integration tasks
- Develop and optimize SQL queries for data extraction, transformation, and loading
- Collaborate with data scientists, analysts, and business stakeholders
- Ensure data integrity, security, and compliance with organizational standards
- Participate in code reviews and contribute to best practices in data engineering
Requirements
- 3-5 years of professional experience in data engineering or related roles
- Strong proficiency in Databricks (including Spark-based data processing)
- Strong programming skills in Python
- Advanced knowledge of SQL for querying and data modeling
- Familiarity with Azure cloud and ADF
- Understanding of ETL frameworks, data governance, and performance tuning
- Knowledge of CI/CD practices and version control (Git)
- Exposure to BI tools (Power BI, Tableau) for data visualization
Benefits
- Flexible work
- Healthcare including dental, vision, mental health, and well-being programs
- Financial well-being programs such as 401(k) and Employee Share Ownership Plan
- Paid time off and paid holidays
- Paid parental leave
- Family building benefits like adoption assistance, surrogacy, and cryopreservation
- Social well-being benefits like subsidized back-up child/elder care and tutoring
- Mentoring, coaching and learning programs
- Employee Resource Groups
- Disaster Relief
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