Top Data Engineer Jobs in Quezon City

Designs, builds, and maintains secure, scalable data solutions across the full data lifecycle, including ingestion, transformation, storage, modeling, and consumption. Develops ETL/ELT pipelines, data platforms, and data products while applying software quality, testing, CI/CD, DevOps, and automation practices. Collaborates with product owners, analysts, software engineers, and global stakeholders to deliver reliable data capabilities for banking and financial services.

Build and modernize enterprise data platforms and batch or real-time pipelines using Azure Data Factory, Databricks, Data Lake, Azure SQL, Python, PySpark, and SQL. Develop ETL/ELT processes, data architectures, APIs, datasets, CI/CD automation, and analytics solutions. Provide Level 2/3 production support, troubleshoot pipeline and data quality issues, resolve incidents, perform root cause analysis, maintain documentation, and support reporting through Power BI. Collaborate with business and technology teams while mentoring junior colleagues.

Designs and implements cloud, DevOps, and data engineering solutions; owns internal and vendor-sourced data; onboards data sources; improves ingestion, governance, controls, storage architecture, and ETL processes. The role also documents processes, collaborates with business and IT teams, manages ad hoc ingestion, and develops testing plans to ensure reliable, accurate data pipelines.

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.

Design, build, and maintain scalable data ingestion, transformation, modeling, warehousing, and ETL/ELT pipelines from diverse high-volume sources. Ensure data accuracy, security, validation, and efficient delivery for analytics and reporting. Improve infrastructure and automate processes while collaborating with stakeholders, documenting solutions, supporting team members, and modeling datasets for BI tools such as Power BI, Tableau, and Apache Superset.

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.

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.

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

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.

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.

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, 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.

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