Senior Data Engineer
- Development and optimization of large-scale data processing workflows using Apache Spark, PySpark, and SQL
- Building and enhancing multi-stage ETL pipelines with idempotency and failure recovery
- Implementation and configuration of pipeline orchestration using Airflow, Dagster, or Prefect
- Working with OLAP databases and optimizing bulk data loading and batch processing
- Integration of data from internal and external sources
- Supporting the transition of the solution into production including automation, monitoring, and collaboration with ML and DevOps teams
- At least 5 years of experience as a Data Engineer
- Minimum 3 years of hands-on production experience with Apache Spark and PySpark
- At least 2 years of experience working with large volumes of customer data in banking, fintech, or telecommunications
- Expert-level SQL skills including optimization of complex analytical queries, window functions, partitioning, and batch processing of large datasets
- Strong Python skills and practical experience with pandas, pyarrow, and Parquet
- Experience building fault-tolerant multi-stage ETL pipelines including idempotency, failure recovery, and memory consumption control
- Hands-on experience with OLAP databases, bulk data loading, and orchestration tools such as Airflow, Dagster, or Prefect
- Opportunity to participate in a large-scale technology project in the financial sector
- Work with high-load systems and large volumes of data
- One-year contract
- Office-based work in Uzbekistan
- Final offer determined based on interview results, candidate’s experience, and technical expertise
- Work within an experienced team of Data Engineering, Machine Learning, and DevOps specialists