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.