Top Data Engineer Jobs in the Philippines

Design and implement BigQuery-based medallion (lakehouse) architectures and ingestion pipelines on GCP; define datasets, partitioning, clustering, and cost controls; support batch/incremental/on-demand processing; implement data quality, monitoring, and search/AI integration; produce architecture docs and work with engineering, data, DevOps, and product teams for handover.

Leads hands-on engineering for modernization of an enterprise eDiscovery data platform. Designs and codes Java services, Oracle PL/SQL, Python utilities, APIs, pipelines, and integrations across on-premise and GCP environments. Owns legacy decomposition, data modeling, ingestion, reconciliation, auditability, observability, performance, security, testing, and controlled migration. Influences technical direction through implementation, code reviews, design forums, mentoring, and production support while ensuring regulated-enterprise requirements are met.

Build and support production API integrations, two-way synchronization, data-quality controls, automation, reporting, and AI-assisted workflows. Design data warehouse and lakehouse components, including staging, entity matching, incremental loading, and history handling. Translate finance, sales, and operations requirements into documented solutions, dashboards, and reliable processes. Manage vendor API constraints, monitoring, reconciliation, security, source control, peer review, and production delivery.

Design, build, and maintain scalable data pipelines and storage on cloud platforms. Implement ETL, data models, data quality monitoring, governance, and collaborate with data scientists for analytics and reporting.

The role involves building and maintaining data pipelines, developing ETL components, testing, CI/CD deployment, and collaborating with the BA and Scrum Master on projects.

Designs and maintains scalable ETL/ELT pipelines and AWS data lake solutions integrating marketing, web analytics, campaign, and engagement data. Develops API, webhook, and batch integrations; ensures data quality, normalization, deduplication, validation, governance, and privacy compliance. Monitors pipeline reliability, writes documentation and tests, and collaborates with stakeholders and analysts to translate business needs into technical solutions using Agile practices.

Design, build, and maintain data storage, ingestion and transformation pipelines. Optimize jobs, automate processes, perform unit and integration testing, monitor platform stability, ensure governance and security compliance, collaborate with stakeholders, document processes, run POCs, and contribute to team best practices to support analytics and data engineering needs.

The Data Developer will design, expand, and optimize data pipelines, process data from various sources, and assist with data-related issues.

Manage and optimize PostgreSQL, SQL Server, and Oracle databases for performance, security, scalability, availability, backup, and recovery. Design schemas, indexes, and partitions; troubleshoot queries; implement governance and security controls; and support disaster recovery planning. Translate business needs into data engineering tasks, prioritize team work, lead standups, guide Power BI solutions, mentor colleagues, and apply AI and automation to improve database operations, testing, reliability, and delivery efficiency.

Design, implement, and maintain scalable data infrastructure for analytics and operational decision-making, optimizing data pipelines and ensuring data quality.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Build and maintain data pipelines and databases to enable analytics-driven decision making. Develop ETL/ELT processes using SQL and Python, design dimensional models, automate ingestion and orchestration (Airflow), evaluate data quality, document sources, and collaborate cross-functionally to improve processes and support actuarial analytics.

Design, implement, and support scalable, secure cloud and data platform environments across AWS, Azure, and GCP. The role involves Python automation, Airflow and Databricks administration, Terraform-based infrastructure provisioning, CI/CD and GitOps deployment, Kubernetes, cloud security, self-service platform development, troubleshooting, and collaboration with architects and delivery teams. Requires long-term work from home and permanent night-shift availability.

Design, build, and maintain end-to-end analytics and reporting solutions: ingest, transform, model, and store data; build Cognos and Power BI reports; ensure data quality; analyze datasets and present insights to stakeholders.

Data Engineer II will execute data project initiatives, maintain data applications, automate processes, and mentor junior developers while collaborating with teams.

Design, build, test and maintain ETL pipelines using Python and Jupyter notebooks. Transform vendor and client data, migrate Excel logic to Python, document processes, respond to incidents within SLAs, automate processes, and collaborate with business and technical teams to improve data workflows.

The Data Engineer Consultant captures, transforms, and loads data using Azure services, ensuring data quality and developing ETL pipelines while collaborating with stakeholders.

Design, develop, and maintain complex T-SQL stored procedures and SSIS ETL packages; performance tune databases; perform data analysis, validation, and testing; support production data loads and troubleshoot database issues while collaborating with technical and business teams.

Designs, develops, and maintains secure enterprise data pipelines and ETL/ELT processes integrating government and commercial systems. Ensures data quality, performance, availability, and real-time delivery for logistics, mission support, analytics, and operational decision-making. Responsibilities include database and pipeline optimization, cloud migration support, technical documentation, cybersecurity and governance compliance, troubleshooting, stakeholder coordination, operational exercise support, and mentoring junior personnel.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture; integrate multiple data sources; ensure data quality, governance, security, and performance; monitor pipelines; support BI, analytics, and ML; document models and workflows; collaborate with stakeholders.

Design, develop, and maintain data pipelines and workflows (batch/streaming), ensure data quality and governance, write clean tested code, troubleshoot processing issues, collaborate in agile teams, produce documentation, and support deployments while growing technical skills under senior guidance.

Lead design and delivery of analytics, automation, and AI-enabled data solutions to improve operational efficiency across Asia Pacific. Build data pipelines, models, dashboards, desktop apps and Excel add-ins; advise stakeholders, manage projects, establish standards, and coach junior team members.

As a Data Platform Engineer, you'll design and maintain scalable data systems, partner with teams on product direction, and ensure system reliability and security.

Design, build, and scale systems to ingest and process large volumes of financial data. Architect data lakes, develop multi-tenant relational databases, evaluate search/vector database solutions, and implement reliable data pipelines and workflow engines supporting mission-critical accounting automation.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.