Menecment İnformasiya Sistemləri şöbəsinin eksperti / Data engineer

About the role

The position is for a Data Engineer within the Management Information Systems department of a bank. The role focuses on designing, developing, testing and maintaining reliable ETL/ELT data pipelines and supporting data integration from core banking and other operational systems.

Key responsibilities

• Design, develop, test and support robust ETL/ELT pipelines for full and incremental data loads.
• Analyse source tables and create source‑to‑target mappings for data integration.
• Design and maintain fact tables, dimensions, data marts and historical data structures.
• Implement data validation, reconciliation, logging, monitoring and error‑handling mechanisms.
• Monitor ETL jobs, investigate failed or delayed loads, and optimise SQL/PLSQL queries and database objects.
• Collaborate with business analysts, report developers, DBAs, information‑security teams and other stakeholders.
• Document technical specifications for data models, ETL flows, dependencies and transformation rules.
• Ensure compliance with information‑security, access‑control, audit and data‑governance requirements.

Required profile

• Minimum 3 years of experience in data engineering, ETL development or database development.
• Strong practical knowledge of SQL and PL/SQL, including procedures, functions, packages and complex queries.
• Experience with at least one enterprise‑grade ETL tool.
• Hands‑on experience with Oracle and/or PostgreSQL relational databases.
• Understanding of ETL architecture, full/incremental loading models and data‑warehouse dimensional modelling.
• Bachelor’s degree in Computer Science, Information Technology, Engineering, Mathematics or a related field.
• Fluent Azerbaijani, intermediate or higher English; Russian is a plus.

Required skills

• SQL, PL/SQL
• Oracle, PostgreSQL
• Enterprise ETL tools (e.g., Informatica, Talend)
• Python for data processing and automation
• Apache Spark / PySpark
• Apache NiFi
• Apache Airflow or similar orchestration platforms
• Apache Kafka or other messaging systems
• Docker, Kubernetes, CI/CD pipelines
• Git version control
• Data‑quality, metadata management, data lineage and data‑governance concepts