Job Description

Position Details:

Reports To: Head of Engineering – Data

About the Role

The Data Engineer is responsible for architecting and maintaining high-performance data pipelines and modeling frameworks. This role focuses on implementing best-in-class tools— specifically SQLMesh and Airflow—to ensure the delivery of high-quality, reliable data to the business. You will lead the migration from legacy dbt models to a modernized SQLMesh environment, ensuring data integrity and performance across our AWS (Redshift) ecosystem.

Key Responsibilities

Migrate & Modernize Data Models: Lead the transition from dbt to SQLMesh, reviewing and refactoring legacy code to improve performance and maintainability.
Orchestrate Complex Workflows: Develop and maintain Airflow DAGs for varied tasks, including SFTP integrations, SQLMesh scheduling, and cross-platform data synchronization.
Manage Data Ingestion: Maintain and optimize AWS DMS and Zero ETL integrations to ensure seamless data flow from transactional databases to Redshift.
Support Analytics Excellence: Collaborate closely with Data Analysts to optimize data structures for QuickSight, ensuring high performance and ease of use for end-users.
Ensure Data Quality: Implement rigorous testing and validation within the modeling layer to provide a “single source of truth” for the organization.
Stakeholder Collaboration: Work with Business Analysts and Engineering leads to translate business requirements into technical data solutions.
Troubleshoot & Optimize: Proactively monitor pipeline health and optimize Redshift performance (distribution/sort keys, query tuning).

Requirements (Essential):

Minimum of 6 years of experience in Data Engineering, with a focus on ELT/ETL patterns.\
Advanced proficiency in SQL and a deep understanding of Data Modeling (Star Schema, Kimball).\ Cloud Native Expertise
Proven experience with AWS services (S3, Redshift, ECS).
Modern Modeling Frameworks: Hands-on experience with dbt is required; experience with (or a strong desire to master) SQLMesh is essential.
Orchestration Experience: Strong ability to build and scale workflows using Apache Airflow.
Collaboration: Excellent communication skills to bridge the gap between technical data infrastructure and business-facing analytics.
Self-Driven: Ability to manage a sprint-based workload independently while maintaining an eye for detail.

Key Technical Skills Required

Languages/Frameworks: SQL (Advanced), Python, SQLMesh, dbt.
Orchestration: Apache Airflow.
AWS Stack: Redshift, S3, DMS, Zero ETL, ECS.
Tools: GitHub (Version Control/Code Reviews), JIRA, Confluence.
BI Support: QuickSight (or similar tools like Tableau/Looker).

Work Details

Shift: Monday to Friday: (AU time – 6:00am 3:00pm or 7:00am- 4:00pm PH Time) ; depending on business needs
Location: Makati | *Work from Home Until Further Notice
Status: Full time employment