Top Data Engineer Jobs in the Philippines

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, support data migration across systems, optimize workflow performance and reliability, troubleshoot data issues, and collaborate with stakeholders on scalable data-driven solutions.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, optimize workflows, troubleshoot data issues, and support scalable data integration. Collaborate with cross-functional teams, document processes, and contribute to data-driven solutions. The role requires Databricks expertise, cloud data-platform familiarity, and understanding of data governance and quality assurance.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, integrate and transform data across systems, optimize workflows, troubleshoot data issues, and document processes. Collaborate with cross-functional teams to deliver scalable, reliable solutions and support data-driven decision-making, with a focus on Databricks and cloud-based data processing.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, integrate and migrate data across systems, optimize workflows, troubleshoot issues, and document processes. Collaborate with cross-functional teams to deliver scalable solutions and support data-driven decision-making. The role requires Databricks expertise and is based in the Manila office.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, integrate and transform data across systems, and optimize workflow performance and reliability. Collaborate with cross-functional teams to understand requirements, troubleshoot data issues, document processes, and support scalable, data-driven solutions. The role requires Databricks expertise and at least one year of relevant experience.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, integrate and transform data across systems, and optimize workflow performance and reliability. Collaborate with cross-functional teams to understand requirements, troubleshoot data issues, document processes, and deliver scalable solutions that support data-driven decision-making.

Design, develop, and maintain data solutions, pipelines, and ETL processes. Ensure data quality, integrate and transform data across systems, monitor and optimize workflows, troubleshoot data issues, and document processes. Collaborate with cross-functional teams to understand requirements and deliver scalable, reliable solutions. The role requires expertise in Databricks and at least one year of relevant experience.

Designs, develops, and maintains data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration across systems, monitors and optimizes workflows, troubleshoots data issues, documents processes, and collaborates with cross-functional teams. The role requires Databricks expertise, cloud data processing familiarity, data governance knowledge, and independent contribution to team solutions.

Designs, develops, and maintains data solutions, pipelines, and ETL processes. Ensures data quality, supports data migration across systems, monitors and optimizes workflows, troubleshoots data issues, and documents processes. Collaborates with cross-functional teams to understand requirements and deliver scalable, reliable solutions. The role requires Databricks expertise, knowledge of data integration and transformation, cloud data platforms, governance, and quality assurance.

Designs, develops, and maintains data solutions, pipelines, and ETL processes. Ensures data quality, integrates and migrates data across systems, and optimizes workflow performance and reliability. Collaborates with cross-functional teams to understand requirements, troubleshoot data issues, document processes, and deliver scalable solutions. Requires Databricks expertise, knowledge of data integration and transformation, cloud data solutions, governance, and quality assurance.

Designs, builds, and maintains data infrastructure supporting analytics and machine learning. Responsibilities include developing scalable data pipelines, data warehouses, data lakes, ETL processes, analytics tools, and workflow automation. The role also ensures data quality and security, supports stakeholders with technical data issues, and maintains compliance with company standards.

Develops and supports ETL/ELT pipelines and API or batch ingestion workflows for marketing and business data. Integrates structured and semi-structured data into AWS data lakes, applies transformations and quality validation, monitors pipeline performance, and troubleshoots issues. Collaborates with engineers, analysts, and stakeholders, maintains documentation, follows Git-based development and Agile practices, and implements governance, privacy, access, and retention requirements.

Support and enhance real-time market data platforms across Linux-based systems and cloud infrastructure. Maintain feeds, monitor data quality and latency, troubleshoot production issues, optimize high-throughput pipelines, implement automation and alerting, perform capacity planning, and ensure regulatory and exchange-policy compliance. Collaborate with trading, product, engineering, and operations teams while providing occasional out-of-hours incident and deployment support.

Design, develop, and maintain scalable ETL/ELT pipelines in Azure (ADF, Databricks); build and govern data models and dictionaries; integrate and ensure data quality; automate pipelines with DevOps/CICD; collaborate with data scientists, analysts and business stakeholders; perform performance tuning and capacity planning. Hybrid role with night-shift schedule.

Own and scale production data platforms for systematic credit strategies. Architect reliable pipelines and storage for large financial datasets, implement governance, testing, validation, CI/CD, APIs, and observability, and collaborate with quantitative researchers and engineers to deliver investment data systems.

Designs, builds, and operates production data pipelines on Databricks using Delta Lake, Unity Catalog, Workflows, PySpark, SQL, and Python. Ingests and transforms enterprise, SaaS, file, and API data into governed, certified datasets. Responsibilities include performance tuning, monitoring, incident response, documentation, CI/CD, data-quality controls, and collaboration with analysts, data scientists, architecture, and governance teams.

Designs, builds, and maintains scalable cloud data pipelines and integrations supporting analytics, AI, reporting, and business transformation. Responsibilities include ETL/ELT development, source system integration, data modeling, quality monitoring, governance, performance optimization, troubleshooting, documentation, and reusable engineering standards. The role collaborates with technical, business, analytics, and client stakeholders to translate requirements into reliable data solutions.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Develops, maintains, and optimizes SQL Server databases supporting enterprise applications. Responsibilities include writing complex T-SQL queries and procedures, profiling data, improving data quality and performance, documenting schemas and data flows, supporting ETL processes, and ensuring secure coding and HIPAA compliance. The role collaborates with cross-functional teams to deliver data milestones and application improvements. This is a work-from-home position supporting US hours and night-shift schedules.

Designs, builds, and maintains scalable ETL/ELT pipelines and data architectures supporting Denials AI. Responsibilities include PostgreSQL data modeling, SQL optimization, AWS data services, pipeline monitoring, incident response, data quality, governance, HIPAA compliance, infrastructure automation, documentation, code reviews, and mentoring junior engineers.

Develop and maintain complex data pipelines, APIs, statistical models, and advanced analytics solutions for commercial insurance and risk consulting. Use Python, PySpark, SQL, Databricks, AI, machine learning, and predictive modeling to produce data-backed consulting outputs and support new product development. The role also involves researching client risk areas, preparing recommendations, and consulting with senior colleagues on financial and statistical analyses.

Operate, monitor and improve production DataOps/ETL workloads for SLHK: manage enterprise schedules and SLAs, lead incident response, execute controlled changes, run data quality checks with evidence, maintain runbooks, and publish/automate service reporting.

Designs, develops, optimizes, and supports ETL and data integration processes using IBM DataStage and Oracle Database 19c. Responsibilities include creating complex SQL queries, managing data transformations, validating data quality, troubleshooting processing issues, tuning performance, monitoring scheduled jobs, and supporting production ETL applications across enterprise systems.

Design and maintain Azure-based data pipelines, ingestion workflows, layered lakehouse transformations, dimensional models, and orchestration processes. Migrate legacy reporting logic, implement data quality testing, maintain documentation, support CI/CD through Azure DevOps, and collaborate with BI teams on curated datasets and report dependencies. Monitor platform performance and handle incremental loading, schema drift, retries, and change data capture.

Lead enterprise data modernization by migrating legacy reporting environments to cloud platforms, building scalable data pipelines and transformation frameworks, optimizing BI models and SQL workloads, and ensuring data quality. Partner with data scientists, analysts, and business stakeholders to deliver governed data products supporting analytics, reporting, and machine learning. Establish best practices for governance, version control, documentation, deployment, monitoring, and platform performance.