Data Engineer( up to 200K php)
Summary
Builds and maintains ETL pipelines and data models using Airflow and dbt to transform raw data into clean, governed layers for analytics and reporting.
Job Description: Data Engineer – Enterprise Data Platform
Role Summary: The Data Engineer will work closely with data architects, business analysts, governance teams, platform teams, and project stakeholders to implement data pipelines and data models aligned with signed‑off BRD, FRS, and Low‑Level Design documents.
Key Responsibilities
- Develop reusable ETL pipelines using Airflow and dbt Core.
- Build SQL‑based transformation models using dbt-spark or an agreed adapter.
- Implement Bronze → Silver → Gold data transformation flows based on signed‑off designs.
- Develop data cleansing, standardization, aggregation, matching, and enrichment logic for platform and business use cases.
- Support ETL exception handling, retry logic, audit logging, and operational monitoring.
- Implement logical and physical data models across Bronze, Silver, and Gold layers.
- Optimize transformation logic and data models for query efficiency, scalability, and maintainability.
- Implement embedded data quality checks in ingestion and ETL processes.
- Support integration of data quality outputs, metadata, and lineage artifacts with Alation.
- Generate dbt artifacts and metadata needed for cataloging and lineage visibility.
- Ensure data engineering outputs are traceable, governed, and aligned with access and classification requirements.
CI/CD and Deployment
- Use GitLab for version control, code review, and CI/CD pipeline execution.
- Support deployment of Airflow DAGs, dbt models, scripts, and configuration across Development, UAT, and Production environments.
- Maintain deployment scripts, technical documentation, and runbooks.
Required Skills and Experience
- Hands‑on experience in data engineering, ETL development, and data lakehouse implementation.
- Experience with Airflow for orchestration.
- Experience with dbt Core for transformation development, testing, and documentation.
- Experience using GitLab or equivalent version control and CI/CD tool.
- Understanding of data quality checks, audit logging, lineage, and catalog integration.