Data Engineer | Mandaluyong (Hybrid)
Summary
Data engineer building and supporting ETL/ELT data pipelines (Python, SQL, Airflow/Dagster, AWS) for business and product needs in a hybrid Mandaluyong role — monitoring pipeline health, handling data-quality issues within SLAs, and maintaining docs, tests, and CI/CD practices.
Work Setup: Monday to Friday, Hybrid (2 days onsite, 3 days wfh)
Location: Mandaluyong
Key Responsibilities:
Support the development and deployment of data pipelines for high-priority business requirements and product initiatives, working under the direction of senior team members.
Monitor pipeline performance and availability, address routine data quality concerns within expected timelines, and escalate complex technical issues when necessary.
Handle data-related requests and incidents within established service-level agreements (SLAs), ensuring workflows run properly through standard procedures, testing, and deployment practices.
Keep pipeline documentation and test coverage up to date while following established practices for CI/CD, data validation, governance, and system monitoring.
Work with cross-functional teams through technical discussions and knowledge-sharing sessions, providing timely updates on potential risks, challenges, and project blockers.
Qualifications:
Bachelor’s degree in IT, Computer Science, Engineering, Mathematics, or a related field, with 2–3 years of experience in data, analytics, or software engineering.
Proficient in Python for scripting and data processing, and SQL for querying, joining, filtering, and aggregating data.
Hands-on experience building ETL/ELT pipelines using databases, data warehouses, cloud platforms (AWS preferred), and orchestration tools such as Airflow or Dagster.
Familiarity with Git, Docker, dbt, Terraform, and database migration tools such as Alembic or Atlas.
Skilled in writing maintainable code, documenting processes, implementing data quality checks, and testing, with a strong willingness to learn new technologies.