Top Data Engineer Jobs in the Philippines

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.

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

Develop, test, and maintain enterprise applications and services; build APIs and system integrations; collaborate with business and technical teams in Agile; troubleshoot defects and performance issues; maintain documentation and support deployments.

Build and maintain cloud-based data pipelines and architectures using SQL and Python. Collaborate with modelers to assemble large datasets, automate and optimize ETL processes, ensure data quality, document technical solutions, and explore new data sources to support predictive analytics.

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.

Provide technical leadership and mentorship to software engineering teams, collaborate cross-functionally to define requirements, manage project tasks/timelines/budgets, identify and mitigate risks, participate in strategy and technology assessments, and promote a culture of innovation and continuous learning.

Lead applications business development, account management, and service delivery for large accounts. Develop operational plans and budgets, resolve staffing and client issues, grow accounts through strategy and solutions, hire and coach staff, ensure contract compliance and client satisfaction, and collaborate on delivery models and financial targets.

Support and optimize large-scale Spark/YARN jobs and storage (S3/EMR). Build job-profiling, cost-analysis, AI-assisted automation, monitoring/alerting (Prometheus, Flink/Spark), and internal ops tooling to improve stability and reduce costs.

Design, build, and maintain scalable data pipelines and ETL processes using Python and SQL. Optimize schemas and queries, deploy/manage data workloads on AWS, and collaborate with analysts and business teams to ensure data accuracy, availability, and governance.

Design, develop, and maintain scalable ELT/ETL data pipelines and reusable data models using Azure Data Factory, dbt, Snowflake, SQL, and Python. Collaborate with Marketing and business stakeholders to translate requirements, ensure data quality, optimize Snowflake performance, monitor production pipelines, maintain documentation and data lineage, and mentor junior engineers.

The Data Engineer will analyze raw data, build data systems, enhance data quality, develop tools, and meet business objectives.

The Data Engineer will analyze raw data, build data systems, enhance data quality, evaluate business needs, and develop analytical tools.

The Data Engineer will analyze and organize raw data, build data systems and pipelines, and improve data quality for business needs.

The Data Engineer will analyze raw data, build data pipelines, evaluate business needs, enhance data quality, and develop analytical tools.

The Data Engineer will analyze and organize raw data, build systems and pipelines, and develop analytical tools to enhance data quality.

Design, build, deploy, and maintain data processing applications and infrastructure; perform ETL, monitor production, resolve incidents, support stakeholders, and create proofs-of-concept and documentation.

Design, maintain, and optimize SQL stored procedures, create SSIS packages, work with databases, analyze data flow, and ensure data quality.

Work with client teams to define test strategy, build automation frameworks, and validate quality across web and mobile. Implement automated testing using Playwright and Maestro, apply AI-assisted workflows to accelerate test design and maintenance, and contribute to platform-level quality practices within a hybrid delivery model.

Serve as a trusted, client-facing data engineering lead for MSP customers: design, maintain and monitor automated ETL/ELT data pipelines and data infrastructure on GCP, troubleshoot and resolve platform issues, maintain data models for analytics, implement best practices, document managed-service processes, mentor junior engineers, and coordinate quarterly customer reviews and implementations.

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.

Design and implement scalable data and AI solutions. Build and optimize EL(T) pipelines, manage data extraction/integration/storage, apply software engineering best practices, mentor team members, collaborate with stakeholders, and drive AI/data platform adoption across projects.

Monitor daily data intake across channels, perform QA to detect inconsistencies or unusual trends, troubleshoot ingestion issues, document findings, and propose process/product improvements while working scheduled shifts.

Lead technical delivery of large-scale data platform and BI solutions: gather requirements from clients, design and implement data platforms, build ETL/ELT pipelines, perform data modelling and SQL performance tuning, provide documentation, training and knowledge transfer, and advise on profiling, integration and platform performance/cost.

Builds and maintains manufacturing analytics tools and software to improve cost, performance, and quality. Integrates AI solutions for shopfloor use, supports applications like Label AI Verify, Transparent Line, and Material Release Tool, and ensures continuous system uptime and process improvements.

The Senior Data Engineer will design and build data infrastructure and pipelines, enhance AI capabilities, and collaborate with teams on data solutions while mentoring junior engineers.