Top Data Engineer Jobs in Quezon City

Support design, develop, test and deploy ETL/ELT pipelines for Finance data lake and warehouse. Provide L1/L2 platform support, implement data quality, validation and governance, maintain metadata/lineage, and collaborate with business users and analysts to translate requirements and enable BI self-service.

Support and optimize Snowflake-based data platforms, pipelines, integrations, and SQL transformations. Investigate incidents, perform root cause analysis, improve performance and reliability, support releases, maintain security and governance controls, monitor data quality, participate in on-call support, communicate with stakeholders, and document operational procedures and fixes.

Assist in building, testing, and maintaining ETL/ELT data pipelines, write and optimize SQL, implement data quality checks, document data models, monitor jobs, and collaborate with data scientists and analysts to deliver scalable data infrastructure and pipelines.

Support pre- and post-deployment data pipeline and product integration activities. Assist clients with data ingestion and configurations, monitor and maintain pipelines (e.g., Matillion), troubleshoot scripts and integration issues (PHP, Postgres), document procedures, and escalate complex problems to senior engineers while collaborating with support and product teams.

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.

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 Engineer will develop and operate a cloud-based Manufacturing Data Lake, manage data pipelines, optimize solutions, and ensure efficient platform use for users.

Design, build and optimize complex Azure-based data pipelines and ETL solutions using Informatica and big-data technologies. Analyze sources, resolve data quality issues, implement automation, tune performance, and mentor junior team members to deliver end-to-end BI and real-time analytics solutions.

Design and maintain secure data platforms, optimize data workflows and processes, and collaborate with stakeholders to implement data solutions.

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

Design, build, and maintain end-to-end data pipelines and AWS infrastructure to support trade and communications surveillance. Implement automated data quality/completeness checks, reconciliation, lineage, retention, testing, monitoring, and recovery. Collaborate with stakeholders, compliance, and cloud teams to ensure regulatory, security, and performance requirements are met for structured and unstructured surveillance data.

Build, scale, and maintain end-to-end batch and streaming ETL/ELT pipelines on Databricks/Spark over AWS. Shape lakehouse architecture on S3/Delta Lake, optimize Spark performance, manage Databricks workflows and clusters, implement Unity Catalog governance, and ensure data correctness for analytics and product teams.

Design, develop, optimize, and maintain ETL/data integration processes using IBM DataStage and Oracle Database 19c. Build and tune complex SQL queries, perform data validation, troubleshoot and support scheduled ETL jobs, and ensure data quality and reliability in production environments.

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 data systems, evaluate business needs, enhance data quality, and develop analytical tools while managing raw data.

Design, build, and maintain data pipelines and databases to support actuarial analytics. Perform data manipulation with SQL/Python, ensure data quality, document sources, automate ingestion and transformations, and collaborate across teams to optimize data architecture and processes.

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.