Kumparan
Data Engineer
Data Engineer in Jakarta who builds and maintains ETL/ELT data pipelines, transformations, and dimensional data models. Core stack includes SQL, Python, PostgreSQL/MySQL, BigQuery, orchestration tools like Airflow or Mage.ai, dbt, and cloud platforms (GCP/AWS/Azure).
- Compensation
- Not disclosed
- Location
- Jakarta, Indonesia
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 1+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor's degree in Computer Science, IT, Engineering, or related field, or equivalent practical experience
- 1-3 years of experience in Data Engineering, Data Analytics, Software Engineering, or related technical role
- Experience developing, maintaining, and optimizing ETL/ELT data pipelines
- Experience with relational databases, data warehouses, and large datasets
- Understanding of data modeling, data transformation, and data integration concepts
- Familiarity with batch processing and basic data orchestration workflows
- Experience integrating data from multiple sources including databases, APIs, and files
- Understanding of data quality, consistency, validation, and monitoring practices
- Familiarity with cloud-based data platforms such as GCP, AWS, or Azure
- Ability to troubleshoot data pipeline issues and collaborate with cross-functional teams
- Willingness to learn new technologies and follow data engineering best practices
- Strong SQL: joins, aggregations, CTEs, window functions, query optimization
- Proficiency in Python for data processing, automation, and pipeline development
- Familiarity with analytical databases such as Google BigQuery
- Familiarity with workflow orchestration tools such as Apache Airflow or Mage.ai
- Understanding of data transformation frameworks such as dbt
- Knowledge of dimensional modeling, fact and dimension tables, star schema design
- Basic knowledge of cloud storage, compute, and managed data services
Full description
Scope of Work:
- SQL: Strong understanding of SQL queries, joins, aggregations, Common Table Expressions (CTEs), window functions, and query optimization.
- Programming: Proficiency in Python for data processing, automation, and pipeline development.
- Databases: Hands-on experience with relational databases such as PostgreSQL or MySQL.
- Data Warehousing: Familiarity with analytical databases such as Google BigQuery
- ETL/ELT Development: Ability to build and maintain data ingestion, transformation, and loading processes.
- Workflow Orchestration: Familiarity with Apache Airflow, Mage.ai, or similar workflow orchestration tools.
- Data Transformation: Understanding of data transformation frameworks such as dbt or equivalent SQL-based transformation approaches.
- Data Modeling: Knowledge of dimensional modeling, fact and dimension tables, and star schema design.
- Cloud Technologies: Basic knowledge of cloud storage, compute, and managed data services.
- Version Control: Experience using Git for source code management and collaboration.
- Data Quality: Ability to implement basic data validation, logging, and pipeline monitoring.
- Problem-Solving: Strong analytical thinking, debugging skills, and ability to investigate data inconsistencies.
- Collaboration: Ability to communicate technical concepts clearly and work effectively with analysts, engineers, and business stakeholders.
Requirements:
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
- 1–3 years of experience in Data Engineering, Data Analytics, Software Engineering, or a related technical role.
- Experience developing, maintaining, and optimizing ETL/ELT data pipelines.
- Experience working with relational databases, data warehouses, and large datasets.
- Understanding of data modeling, data transformation, and data integration concepts.
- Familiarity with batch processing and basic data orchestration workflows.
- Experience integrating data from multiple sources, including databases, APIs, and files.
- Understanding of data quality, consistency, validation, and monitoring practices.
- Familiarity with cloud-based data platforms such as Google Cloud Platform (GCP), AWS, or Azure.
- Ability to troubleshoot data pipeline issues and collaborate with cross-functional teams.