Senior Data Engineer (Databricks) – Financial Services
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REMODEVS SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ Senior Data Engineer (Databricks) – Financial Services
Summary
Senior Data Engineer for a 12-month project serving a financial-services client in Warsaw. Day to day: designing, building and optimizing large-scale ETL/ELT pipelines and Delta Lake lakehouse solutions on Databricks, using PySpark, SQL, and Python on a cloud platform (Azure/AWS/GCP), with attention to performance, governance and compliance.
Nasze wymagania:
- 5+ years of commercial experience in Data Engineering.
- Minimum 3 years of hands-on experience with Databricks.
- Strong practical knowledge of Apache Spark and PySpark.
- Advanced SQL and Python programming skills.
- Experience designing and implementing ETL/ELT pipelines.
- Solid understanding of Delta Lake and Lakehouse architecture.
- Experience working with cloud platforms (Azure, AWS, or GCP).
- Knowledge of data modeling, data warehousing, and data integration.
- Experience optimizing data processing performance and managing large datasets.
- English proficiency at C1 level.
Mile widziane:
- Previous experience in banking, insurance, or financial services.
- Experience with Azure Databricks and Azure Data Factory.
- Knowledge of Unity Catalog and Databricks Workflows.
- Experience with CI/CD pipelines and Infrastructure as Code.
- Databricks certifications.
- Understanding of data governance, security, and regulatory requirements in financial environments.
O projekcie:
We are looking for an experienced Senior Data Engineer to join a long-term project for a client in the financial services industry. The ideal candidate has strong hands-on experience with Databricks, modern data engineering technologies, and building scalable data solutions in enterprise environments.
Project Details
- Position: Senior Data Engineer (Databricks)
- Industry: Financial Services
- Seniority: Senior
- Start Date: December 2026
- Duration: 12 months
- Language: English C1
Zakres obowiązków:
- Design, develop, and maintain scalable data pipelines using Databricks.
- Build and optimize ETL/ELT processes for large-scale data processing.
- Develop data transformation solutions using PySpark, Python, and SQL.
- Implement and optimize Delta Lake architectures.
- Design and maintain reliable data ingestion and integration processes.
- Improve data quality, performance, and reliability across data platforms.
- Collaborate with Data Architects, Analysts, and business stakeholders to deliver data solutions.
- Ensure data security, governance, and compliance with financial industry standards.
- Troubleshoot and optimize existing data engineering workflows.