Posted May 6, 2026
As a Data Engineer at Socit Gnrale, you will be responsible for developing, optimizing, and maintaining scalable data pipelines using Python and Apache Spark on Databricks to process large volumes of data from multiple sources. You will design and implement ETL/ELT workflows to ingest, cleanse, transform, and aggregate metrics according to business requirements. Collaboration with data scientists, analysts, and business stakeholders is essential to understand data needs and translate them into technical solutions. Implementing data quality checks, validation rules, and monitoring frameworks will be part of your responsibilities to ensure accuracy and reliability of metrics. Building and maintaining automated reporting and dashboarding solutions leveraging Databricks and associated BI tools is key to providing valuable insights to stakeholders. Key Responsibilities:
Develop, optimize, and maintain scalable data pipelines using Python and Apache Spark on Databricks. - Design and implement ETL/ELT workflows to ingest, cleanse, transform, and aggregate metrics. - Collaborate with data scientists, analysts, and business stakeholders to understand data needs. - Implement data quality checks, validation rules, and monitoring frameworks. - Build and maintain automated reporting and dashboarding solutions. - Manage version control, code reviews, and CI/CD pipelines. - Troubleshoot and resolve performance issues and optimize Spark jobs. - Stay current with regulatory requirements and integrate relevant data sources. - Work in agile mode in complex contexts, sharing best practices within a team. - Translate functional requirements into technical solutions. - Provide business analyst support for functional testing and diagnostics. Qualifications Required:
4+ years of Python programming experience in data manipulation libraries (Pandas, NumPy). - Strong experience with Apache Spark (PySpark) on Databricks platform. - Expertise in building ETL/ELT pipelines and data workflows in Databricks. - Experience in data architecture design and implementation. - Familiarity with SQL and NoSQL databases. - Experience with cloud platforms (preferably Azure or AWS). - Strong experience in version control systems (Git) and CI/CD tools. - Experience with data quality frameworks and monitoring tools. - Experience with containerization (Docker) and orchestration (Kubernetes). - Strong analytical skills and ability to work in an agile development environment. At Socit Gnrale, we believe in recognizing and promoting all talents, regardless of their background, and are dedicated to supporting our Groups ESG strategy by implementing ESG principles in all our activities and policies. As a Data Engineer at Socit Gnrale, you will be responsible for developing, optimizing, and maintaining scalable data pipelines using Python and Apache Spark on Databricks to process large volumes of data from multiple sources. You will design and implement ETL/ELT workflows to ingest, cleanse, transform, and aggregate metrics according to business requirements. Collaboration with data scientists, analysts, and business stakeholders is essential to understand data needs and translate them into technical solutions. Implementing data quality checks, validation rules, and monitoring frameworks will be part of your responsibilities to ensure accuracy and reliability of metrics. Building and maintaining automated reporting and dashboarding solutions leveraging Databricks and associated BI tools is key to providing valuable insights to stakeholders. Key Responsibilities:
Develop, optimize, and maintain scalable data pipelines using Python and Apache Spark on Databricks. - Design and implement ETL/ELT workflows to ingest, cleanse, transform, and aggregate metrics. - Collaborate with data scientists, analysts, and business stakeholders to understand data needs. - Implement data quality checks, validation rules, and monitoring frameworks. - Build and maintain automated reporting and dashboarding solutions. - Manage version control, code reviews, and CI/CD pipelines. - Troubleshoot and resolve performance issues and optimize Spark jobs. - Stay current with regulatory requirements and integrate relevant data sources. - Work in agile mode in complex contexts, sharing best practices within a team. - Translate functional requirements into technical solutions. - Provide business analyst support for functional testing and diagnostics. Qualifications Required:
4+ years of Python programming experience in data manipulation libraries (Pandas, NumPy). - Strong experience with Apache Spark (PySpark) on Databricks platform. - Expertise in building ETL/ELT pipelines and data workflows in Databricks. - Experience in data architecture design and implementation. - Familiarity with SQL and NoSQL databases. - Experience with cloud platforms (pr
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