Senior Data Engineer

Our Clients; Pasig; Hybrid; PHP 1,200,000 - 1,800,000; Full time

Job summary

Our Clients in Pasig is seeking a Senior Data Engineer to own major sections of the data platform, delivering timely, well-structured data to internal stakeholders. The role blends hands-on pipeline work with alignment to architecture guided by the Data Engineering Lead.

Reporting to the Data Engineering Lead, you will mentor junior engineers, collaborate with cross-functional teams, and ensure secure, reliable pipelines for diverse data sources, including external/unstructured data.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 3-4 years of experience in data engineering or related technical role.
• Proficiency in Python for ETL/ELT and advanced SQL.
• Hands-on experience with modern orchestration tools (Dagster, Airflow).
• Experience with at least one major cloud platform and cloud data warehouses.
• Strong communication and leadership abilities.
• Problem solving in ambiguous environments.

Responsibilities

• Design and scope new data pipelines and solutions for review by the Data Engineering Lead.
• Lead development and maintenance of complex, secure data pipelines.
• Own core data infrastructure components for reliability and performance.
• Review code and merge requests to ensure quality and standards.
• Mentor junior data engineers and provide day‑to‑day technical guidance.
• Coordinate with IT/security for uptime and security of pipelines.

Skills

Python for data engineering

Advanced SQL

Dagster/Airflow

Cloud data warehousing

Communication & leadership

Problem solving

Education

Bachelor’s degree or equivalent

Tools

Dagster

Airflow

Redshift

BigQuery

Snowflake

Job description

Senior Data Engineer(Health Services)

Location: Pasig

Work Schedule: Hybrid (3 days Onsite, 2 days Work From Home)

Role Overview

The Senior Data Engineer owns major sections of the data platform and infrastructure, delivering accurate, timely, and well-structured data to key internal stakeholders and business groups. This role combines hands‑on delivery of complex pipeline and infrastructure work with adherence to the standards, architecture, and direction set by the Data Engineering Lead.

Reporting directly to the Data Engineering Lead, the Senior Data Engineer serves as the first technical escalation point for junior data engineers and partners closely with cross‑functional teams to ensure reliable, secure, and high‑quality pipelines for diverse data needs, including a growing set of external and unstructured sources.

Key Responsibilities

• Architecture & Solution Design: Design and scope new pipelines and solutions, proposing new features or major architectural changes to the team for the Data Engineering Lead's review.
• Pipeline Development & Maintenance: Lead the development and implementation of highly complex, secure, and timely data pipelines while adhering to set architectural standards and design patterns.
• Core Infrastructure Ownership: Own the development and technical expertise of core data infrastructure components to ensure consistent operational efficiency, reliability, and maintainability of production pipelines.
• Code Review & Quality Assurance: Review code, pipeline changes, and merge requests from junior data engineers to ensure outputs meet established data engineering standards.
• Mentorship & Technical Guidance: Co‑mentor junior data engineers alongside the Data Engineering Lead by providing day‑to‑day technical guidance, coaching, and pair programming sessions.
• Incident Management & Escalation: Act as the first technical escalation point for pipeline incidents and data quality issues that exceed junior engineers' scope, escalating cross‑team or high‑complexity issues requiring major architectural shifts to the Lead.
• System Performance & Security: Partner with IT and security teams to maintain uptime, performance, and security of critical data pipelines through active incident response, root cause analysis, rubber duck debugging, and long‑term fix proposals.
• Infrastructure & Network Resolution: Resolve infrastructure, database integration, and network changes that affect pipeline delivery in coordination with IT infrastructure counterparts.
• Cross‑Functional Collaboration: Coordinate closely with business stakeholders, IT, and cross‑functional partners to ensure technical integration and data requirements are met.
• SLA & Optimization: Continuously maintain and improve the speed, robustness, and delivery of pipelines to meet SLA targets using standardized tools and patterns.

Qualifications

Minimum Qualifications

• Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, Mathematics, or a related field, or equivalent practical experience.
• Experience: 3-4 years of experience in data engineering, analytics engineering, software engineering, or a related technical role, with a proven track record of owning, scoping, and delivering end‑to‑end data pipeline features and infrastructure projects.
• Programming & SQL: Demonstrated proficiency in Python for data engineering (ETL/ELT jobs, data processing, orchestration integration) and advanced SQL (complex joins, window functions, data quality checks, and performance optimization).
• Orchestration Tooling: Hands‑on experience maintaining production data pipelines using modern orchestration tools (e.g., Dagster, Airflow, or similar).
• Cloud & Data Warehousing: Experience with at least one major cloud platform (AWS preferred) and cloud data warehouses (e.g., Redshift, BigQuery, Snowflake).
• Communication & Technical Leadership: Strong communication skills with the ability to explain technical concepts clearly and lead technical discussions or cross‑team coordination work.
• Problem Solving: Demonstrated self‑direction and sound judgment in ambiguous environments, with the ability to turn loosely defined problems into actionable deliverables.

Good to Have

• IaC & Containerization: Experience with Infrastructure as Code (Terraform, CloudFormation), Docker, and familiarity with Kubernetes.
• Transformation Frameworks: Experience using transformation frameworks like dbt or equivalent patterns for modular, testable SQL‑based transformations.
• Unstructured & Third‑Party Data: Experience integrating non‑relational, unstructured, or third‑party/external data sources.
• Best Practices: Working knowledge of modern data architecture patterns (e.g., Medallion architecture, SCD-2, dimensional modeling, trunk‑based development).
• Data Privacy: Familiarity with data privacy and security regulations relevant to healthcare, pharma, or regulated industries (e.g., the Philippine Data Privacy Act).