Lead Data Engineer, GCP

Manila Recruitment

. Own the reliability, scalability, and performance of the company's core data pipelines and infrastructure.

Posted 11/2/2026full-timeRemote • PhilippinesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering with a focus on designing, building, and maintaining scalable data pipelines and cloud-based infrastructure. Proficient in Python, SQL, and tools like dbt and Apache Airflow to ensure data quality and operational efficiency.

Highest-signal resume keywords

Data EngineeringPython ProficiencySQL ProficiencyGoogle Cloud Platform (GCP)Apache Airflow

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignData TransformationData ModellingETL/ELT ProcessesPerformance OptimisationData GovernanceWorkflow OrchestrationTechnical Solution DesignData Quality MonitoringAutomation

Soft Skills

Problem-SolvingDecision-MakingEffective CommunicationTechnical GuidanceCollaboration

Tools & Technologies

Dbt (Data Build Tool)Google BigQueryApache AirflowAI-Assisted DevelopmentCloud-Based Data Infrastructure

Industry Keywords

Data ArchitectureData WarehousingAnalytics WorkflowsData Quality IssuesEngineering Standards

Tech Stack

Tools & technologies

AirflowApacheBigQueryCloudETLGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Own the reliability, scalability, and performance of the company's core data pipelines and infrastructure.
Set technical priorities, identify risks, and drive improvements to ensure data remains accurate, accessible, and dependable as the business grows.
Lead the design and delivery of complex data engineering initiatives from architecture and technical planning through implementation.
Make sound technical decisions, balance competing priorities, and ensure solutions are scalable and maintainable.
Establish and reinforce engineering standards across Python, SQL, orchestration, testing, documentation, data governance, and monitoring.
Leverage AI and automation to reduce repetitive tasks, including test and documentation generation and early detection of data quality issues.
Set technical direction and quality standards for data engineering.
Conduct design and code reviews, share technical knowledge, and support onboarding, development, and guidance of engineers.
Partner with Data Science, BI, and business teams to translate data requirements into practical technical solutions.
Anticipate dependencies, resolve complex issues, and strengthen the data foundation supporting analytics, machine learning, and business decision-making.

Requirements

What you’ll need

At least 5 years of hands-on experience in data engineering, particularly in designing, building, and maintaining production data pipelines and cloud-based data infrastructure.
Strong hands-on experience with dbt (Data Build Tool) for data transformation, modelling, and management of analytics workflows.
Solid experience with Google Cloud Platform (GCP), particularly BigQuery and other relevant data processing and storage services.
Proven experience using Apache Airflow for workflow orchestration, scheduling, monitoring, and managing data pipelines.
Strong proficiency in Python and SQL, with the ability to develop scalable data solutions and troubleshoot complex technical issues.
Good understanding of data architecture, data warehousing, ETL/ELT processes, performance optimisation, data quality, monitoring, and governance.
Experience designing technical solutions, leading complex data engineering projects, and taking ownership of delivery and ongoing system performance.
Ability to establish engineering standards, conduct code reviews, provide technical guidance, and share knowledge with other engineers.
Familiarity with AI-assisted development, automation, or modern engineering practices to improve development productivity, data quality, and operational efficiency.
Strong problem-solving and decision-making skills, with the ability to balance technical priorities and business requirements and communicate effectively with technical and nontechnical stakeholders.