data engineer in fintech

The company is hiring for a large-scale technology project in the financial sector.

• Run and maintain production data pipelines regularly;
• Monitor pipeline stability and investigate failures;
• Support scheduled model retraining processes;
• Prepare and maintain operational reporting;
• Work with large volumes of customer and analytical data;
• Collaborate with Data Engineering, Machine Learning, and DevOps teams.

• 4+ Years of experience in Data Engineering;
• Experience working with large volumes of customer data in banking, fintech, or telecom is required;
• Strong SQL skills, including complex query optimization, window functions, partitioning, and batch processing;
• Experience with OLAP databases and high-volume data loading;
• Strong Python skills, including PySpark, pandas, pyarrow, and Parquet;
• Experience with multi-stage ETL pipelines, failure recovery, and idempotent processing;
• Hands-on experience with Airflow, Dagster, Prefect, or similar orchestration tools;
• Nice to have: experience integrating external data sources.

• Opportunity to work on a large-scale financial technology project;
• Complex technical challenges involving high-load systems and large datasets;
• Experienced Data Engineering, Machine Learning, and DevOps team;
• Competitive compensation based on experience and technical expertise.