Job description for Data Engineer (Denado) at PT Achiera Global Utama
Requirements
- Minimum 3 years of experience as a Data Engineer, ETL Developer, or in a similar data engineering role.
- Strong SQL skills and experience with relational databases such as Oracle, PostgreSQL, MySQL, or similar.
- Proficient in Python for data processing, automation, and scripting.
- Hands-on experience in developing and maintaining ETL/ELT pipelines and data integration processes.
- Good understanding of Data Warehouse concepts, data modeling, and data pipeline architecture.
- Experience in query optimization, performance tuning, and troubleshooting data processing jobs.
- Familiar with Linux/Unix environments and Git version control.
- Experience with data quality, data validation, reconciliation, and data transformation.
- Good analytical and problem-solving skills.
- Good communication skills and ability to collaborate with both technical and business stakeholders.
- Experience with Spark/PySpark, Kafka, Airflow, Dataiku, or cloud platforms is a plus.
- Experience in regulatory reporting, financial data, or banking projects is a plus.
Responsibilities
- Design, develop, and maintain ETL/ELT pipelines and data processing workflows.
- Develop and maintain Data Warehouse solutions, data models, and reporting datasets.
- Integrate data from multiple sources and ensure data accuracy, consistency, and reliability.
- Develop Python scripts and automation solutions for data processing and operational tasks.
- Develop and optimize SQL queries, database processes, and ETL jobs.
- Perform data validation, reconciliation, and data quality checks.
- Troubleshoot data pipeline, data quality, and performance issues.
- Monitor and improve the performance, reliability, and scalability of data pipelines.
- Collaborate with Data Analysts, Business Users, Software Engineers, and other stakeholders to deliver data solutions.
- Support data migration, system integration, and other data engineering initiatives.
- Maintain technical documentation for data pipelines, data models, and processes.
- Follow best practices for development, testing, deployment, monitoring, and version control.
- Contribute to improvements in data architecture and data engineering practices.
