Data Engineer | Hybrid Setup

Work Setup: Monday to Friday, Hybrid (2 days onsite, 3 days wfh) Location: Mandaluyong Key Responsibilities: Assist in developing and successfully deploying new data pipelines to support priority use cases and product features under senior guidance. Monitor and maintain pipeline uptime, resolve routine data quality issues within target turnaround times, and escalate complex incidents as needed. Respond to data requests and incidents within defined SLAs while ensuring successful workflow execution through established runbooks, tests, and deployment processes. Maintain technical documentation and basic test coverage for owned pipelines while applying industry best practices in CI/CD, data quality, governance, and monitoring. Collaborate cross-functionally through technical presentations and knowledge-sharing activities, while proactively communicating issues, risks, and blockers to the team. Qualifications: Bachelor’s degree in IT, Computer Science, Engineering, Math (or equivalent experience) with 2–3 years in data, analytics, or software engineering. Proficient in Python (scripting and data processing libraries) and SQL (querying, filtering, aggregations, and joins). Hands-on experience building ETL/ELT pipelines with relational databases, data warehouses, major cloud platforms (AWS preferred, GCP, Azure), and orchestration tools like Dagster or Airflow. Familiarity with Git, Docker, dbt, Terraform (Infrastructure as Code), and schema migration tools (e.g., Alembic, Atlas). Skilled in writing maintainable code, maintaining technical documentation, applying data quality/testing practices (e.g., Pandera), and demonstrating a strong eagerness to learn new technologies.