Data Engineer

Responsibilities

Societe Generale Global Solution Centre (SG GSC) acts as a business solutions center for Société Générale, one of the largest European financial groups. We provide high quality professional services in over 35 countries in various business areas - Finance & Accounting, HR, IT and Corporate Operations. Our mission is to be a partner of choice, valued for owning, transforming and innovating with best-in-class talent.

We are seeking a skilled and motivated Data Engineer to design, develop, and maintain scalable data pipelines and data products. The ideal candidate has strong expertise in Python, SQL, PySpark, and data modeling, with experience delivering reliable data solutions that support reporting, analytics, operational processes, and AI initiatives.

The role requires close collaboration with business stakeholders, analysts, and engineering teams to transform data into actionable business value.

Responsabilities

• Design, develop, and maintain end-to-end data pipelines and workflows.
• Build ETL/ELT processes to ingest, transform, and enrich data from multiple sources.
• Develop scalable data products and reusable data models.
• Implement data transformations using Python, SQL, and PySpark.
• Ensure data quality, consistency, and reliability through testing and monitoring.
• Troubleshoot and optimize data pipelines, processing jobs, and platform performance.
• Collaborate with business stakeholders to translate requirements into technical solutions.
• Support production deployments and ensure operational stability.
• Implement data governance, security, lineage, and access control best practices.
• Integrate data solutions with databases, APIs, and enterprise applications.
• Create and maintain technical documentation.
• Participate in code reviews, design discussions, and Agile ceremonies.

Profile required

• 3-5 years of experience in Data Engineering or related roles.
• Strong proficiency in Python, SQL, and PySpark.
• Experience building and maintaining ETL/ELT pipelines.
• Strong understanding of data modeling and large-scale data processing.
• Experience with distributed data platforms and modern data architectures.
• Knowledge of data quality, governance, security, and monitoring principles.
• Strong analytical, problem-solving, and communication skills.
• Ability to work effectively in cross-functional teams.

• Experience with enterprise data platforms such as Palantir Foundry, Databricks, Snowflake, Synapse, or similar technologies.
• Experience with cloud platforms (Azure, AWS, or GCP).
• Familiarity with CI/CD and DevOps practices.
• Experience with AI/ML, analytics, or data science workflows.
• Knowledge of Financial Services, Risk, Compliance, or AML domains.

• Strong ownership and accountability.
• Ability to translate business requirements into scalable data solutions.
• Continuous learning mindset and focus on improvement.
• Strong collaboration and stakeholder management skills.
• Commitment to quality and delivery excellence.