Top Data Engineer Jobs in Manila

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, 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.

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, build, and maintain Microsoft Fabric data architectures and ETL pipelines (Fabric, Synapse, ADF). Transform and optimize data for analytics and AI, support RAG/LLM workloads, ensure data quality, governance, performance, and document workflows.

Responsible for developing and maintaining ELT snowflake solutions, collaborating with teams, mentoring developers, and improving data engineering practices.

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

Design, build, and maintain scalable ETL and real-time data processing systems on Databricks and AWS. Develop Spark-based pipelines, ensure data quality and governance, implement security controls, automate infrastructure with Terraform, and collaborate with analysts and engineers to resolve issues and optimize performance.

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.

Design, build, and maintain scalable Azure-based data pipelines, ETL/ELT processes, data warehouses, and reusable data services. Provide Level 2/3 support for data platforms, troubleshoot production incidents and data quality issues, implement CI/CD and DevOps automation, and collaborate with business and engineering teams to deliver governed analytics solutions.

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

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.

The Data Engineer will design, develop, and maintain data pipelines and architecture, ensuring data quality and governance, while collaborating with data professionals.

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.

As a Data Engineer, you will analyze information needs, design and deploy data processing applications, maintain infrastructure, and resolve incidents in production while supporting internal staff and stakeholders.

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.

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.

Design, build, and maintain data models, analytics pipelines, and statistical models; perform EDA and validate analyses; ensure data quality and governance; produce visualizations and reports; collaborate with analysts, engineers, and stakeholders to deliver scalable, production-ready data solutions.

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.

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.

Design, build, test, tune, and support production ETL/data pipelines using PySpark, Python, advanced SQL, and AWS data services. Implement CDC, incremental loads, idempotent processing, reconciliation, data quality checks, and maintain documentation, tests, and release support while collaborating with analysts, architects, QA, DevOps, and senior engineers.

Manage and maintain data operations infrastructure and pipelines: design databases and ETL, ensure data quality, automate ingestion/transformation using SQL and Python, troubleshoot issues, collaborate with data teams, and integrate sources like Oracle, JDE, SharePoint, Excel, and SAP to produce usable data products.

The Cloud Data Test Engineer will test cloud-based data solutions, ensure data pipeline quality, perform end-to-end data validation, and create test plans.

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.

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.

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.