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Stacjonarnie
Senior
Senior Data Engineer (Databricks) – Financial Services
REMODEVS SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
⚲ Warszawa, Śródmieście
24 000–29 000 zł netto (+ VAT) / mies.
Wymagania
- Databricks
- Apache Spark
- PyTorch
- Python
- SQL
- Azure DevOps
- Unity 3D
- Azure SQL
Opis stanowiska
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.
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.
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