data engineer in banking

AzerTurkBank is a banking organization that develops and operates data warehouses and data platforms supporting Core Banking, CRM, card systems, risk, finance, retail, and AML business functions.

• Build, develop, and optimize the Bank’s Data Warehouse and data platforms;
• Integrate data from Core Banking, CRM, card systems, and other internal and external sources into the DWH;
• Perform data migration and integration from Oracle databases to PostgreSQL environments;
• Develop and manage ETL/ELT, batch, and real-time data pipelines using Apache NiFi, Kafka, Spark, and Airflow;
• Design Fact/Dimension, Star Schema, and Snowflake Schema data models for the DWH;
• Apply Incremental Load, CDC, and SCD Type 1/2 approaches;
• Monitor data pipelines, investigate issues, and optimize performance;
• Build validation and control mechanisms for data quality, completeness, and consistency;
• Participate in developing data marts for Risk, Finance, Retail, AML, and other business units;
• Participate in implementing data lineage, metadata, data governance, and data architecture standards;
• Automate versioning and deployment of data solutions using Git and CI/CD tools;
• Ensure the data platform operates with resilience, scalability, and information security requirements.

• At least 3 years of practical experience in Data Engineering or Data Warehouse;
• Experience building, developing, and optimizing Data Warehouses and data platforms;
• Strong PostgreSQL knowledge and real-project experience, with the ability to work with Oracle databases;
• Practical experience with Oracle-to-PostgreSQL data migration and cross-system data integration;
• Experience building and managing data ingestion and ETL/ELT pipelines with Apache NiFi;
• Experience building data integration, event streaming, and real-time data flows with Apache Kafka;
• Practical knowledge of processing and transforming large volumes of data with Apache Spark;
• Experience managing orchestration, scheduling, and dependency management for data pipelines with Apache Airflow;
• Knowledge of batch and real-time data processing principles and Data Warehouse modeling, including Fact/Dimension, Star Schema, and Snowflake Schema;
• Practical knowledge of Incremental Load, Change Data Capture (CDC), SCD Type 1, and SCD Type 2;
• Skills in data pipeline monitoring, troubleshooting, and performance optimization;
• Experience with Git, CI/CD principles, and Linux/Unix environments;
• Good understanding of data architecture, data integration, and distributed data processing principles;
• Nice to have: experience with Data Lake/Lakehouse, Ceph or S3-compatible Object Storage, Apache Flink, OpenMetadata, data quality, data lineage, metadata management, data governance, and complex Kafka + NiFi + Spark + Airflow integration projects.

• No conditions specified