Data Engineer
Summary
Designs, builds, and maintains data pipelines, ETL/ELT processes, and a centralized data warehouse/data lake consolidating sources like accounting systems, databases, APIs, and spreadsheets for BI dashboards and reporting. Core stack: SQL (PostgreSQL, MySQL, BigQuery, Snowflake, Redshift), Python, and orchestrators such as Airflow.
- Design , build and manage data warehouse / data lake.
- Design, build, and maintain data pipelines from multiple companies/business units into a centralized data warehouse.
- Develop and manage ETL/ELT processes to ensure data is accurate, clean, and available for reporting.
- Build and maintain data warehouse, data marts, and structured datasets for business intelligence needs.
- Integrate data from various sources such as accounting systems, internal systems, databases, APIs, Excel, and Google Sheets.
- Perform data cleaning, transformation, validation, and standardization across business units.
- Monitor pipeline performance, data freshness, data quality, and system reliability.
- Collaborate with BI Engineer and business stakeholders to prepare data for dashboards and management reports.
- Implement data security, access control, backup, and documentation for data architecture and pipelines.
- Troubleshoot data issues and ensure consistency between source systems and the data warehouse.
Qualifications:
- Bachelor’s degree (S1) in Computer Science, Information Systems, Data Engineering, Statistics, Mathematics, Engineering, or related fields.
- Minimum 2 years of experience as Data Engineer, ETL Developer, Database Engineer, or similar role.
- Strong proficiency in SQL and relational databases such as PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake, or Redshift.
- Experience in ETL/ELT processes, data warehouse design, and data modeling.
- Proficiency in Python for data processing and automation.
- Experience with API integration, data ingestion, and workflow orchestration tools such as Airflow, Prefect, Dagster, or similar.
- Familiarity with cloud platforms.
- Strong understanding of data quality, data governance, and database performance optimization.
- Strong analytical thinking, problem-solving skills, and attention to detail.
- Able to work with cross-functional teams and understand business data needs.