Data Engineer (Banking/Cloud Data Platform)
Summary
Build and maintain cloud-based data pipelines and transformation models for banking clients using Airflow, dbt, and Spark.
Keyrus is an international consulting firm, specializing in the integration of data intelligence and Digital solutions. With over 2,800 employees spread across 29 countries, Keyrus continues to deliver on such projects to a wide range of clients from various industries including but not limited to Banking/Finance, Healthcare/pharmaceuticals, FMCG, Oil & Gas, and more.
As part of Keyrus’ solution delivery, we are also in a position to recruit and place technical consultants to complement on existing client projects with their expertise. As such, we seek innovative and agile people to support ambitious and forthcoming technological challenges.
Keyrus is looking for a Data Engineer to join our Data team in the Philippines and support the delivery of high-impact analytics, and data engineering solutions for enterprise clients across APAC.
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 the signed-off BRD, FRS, and Low-Level Design documents.
Key Responsibilities
ETL and Transformation Development
- Develop reusable ETL pipelines using Airflow and dbt Core
- Build SQL-based transformation models using dbt-spark or agreed adaptered
- Implement Bronze - Silver - Gold data transformation flows based on signed-off design
- 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 requirement
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 runbook
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 tools
- Understanding of data quality checks, audit logging, lineage, and catalog integration