Top Data Engineer Jobs in Manila

Designs, develops, and supports data and application solutions for regulatory reporting across Asian markets. Responsibilities include building ETL pipelines with SQL Server, T-SQL, and SSIS; optimizing databases; translating regulatory requirements into scalable solutions; managing Azure DevOps CI/CD processes; supporting governance, risk, and security standards; troubleshooting issues; documenting systems; and coaching team members.

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.

Build and maintain ETL/ELT pipelines using SQL and Python; integrate batch and near-real-time data; create validated datasets for analytics and machine learning; implement data quality checks; monitor pipelines and support incident resolution; collaborate with analysts and ML engineers; and contribute to documentation and continuous improvement in a cloud-based environment.

Designs, develops, and supports enterprise-scale cloud data pipelines, integrations, ETL/ELT processes, data quality solutions, and Azure data platforms. The role includes monitoring and optimizing pipelines, resolving production incidents, supporting data products, conducting testing, creating documentation, and collaborating with distributed business and technology teams. It also participates in Agile delivery and technology proof-of-concepts.

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.

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 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.

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.

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.

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.

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, 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.

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.

Build and maintain business intelligence data pipelines by extracting data from multiple sources, loading it into Snowflake, transforming it with SQL and dbt, and creating Looker visualizations. Ensure data quality and timeliness while partnering with business intelligence teams to define requirements, develop usable data models, and deliver business insights.

Performs data quality assurance for enterprise data pipelines and warehouses. Designs and executes validation, unit, integration, regression, source-to-target, and automated tests across SSIS, dbt, Fivetran, Python, and Databricks. Reviews requirements, documents defects in Azure DevOps, retests fixes, conducts quality gate checks and post-production audits, and collaborates with analysts and developers in an Agile environment. Uses AI-assisted tools for test generation and query review while validating their output.

Executes data validation, regression, source-to-target, and automated tests using established QA standards. Writes SQL queries to verify data quality, reconciliations, transformations, and row counts; documents defects in Azure DevOps and retests fixes. Supports test data preparation and environment readiness, participates in Agile ceremonies, and completes training in SQL, Python, data warehousing, Databricks, and AI-assisted testing tools.

Manage and optimize PostgreSQL, SQL Server, and Oracle databases for performance, security, availability, backup, recovery, and disaster resilience. Design schemas, indexes, and partitions; troubleshoot queries; implement governance and security controls; and support Power BI data solutions. Translate business needs into engineering work, prioritize team tasks, lead standups, mentor colleagues, collaborate with stakeholders, and apply AI and automation to improve database reliability, testing, delivery speed, and operational efficiency.

Execute structured and exploratory QA tests for gameplay data-collection software, install and maintain software on personal hardware, reproduce and document bugs, analyze logs, and escalate issues. Validate PC hardware, GPU performance, drivers, and redundant network connectivity to ensure reliable data-collection sessions. Communicate concise technical findings in written English through Slack and triage feedback for client engineering teams.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Support and maintain actuarial data assets, ETL processes, reporting platforms, dashboards, and automation solutions. Responsibilities include troubleshooting data discrepancies and failed jobs, validating releases, optimizing SQL queries, supporting Snowflake data structures, enhancing monitoring, automating operational activities, and improving Power BI reports. The role requires collaboration with business users and data management leadership to ensure reliable, accurate, and accessible business-critical data.

Perform automated and manual quality assurance for enterprise data products, pipelines, Databricks Lakehouse layers, Delta Lake tables, and ETL/ELT processes. Design validation, integration, regression, performance, and source-to-target tests; document defects; validate governance, access controls, lineage, and technical specifications; support deployment quality gates; and collaborate with developers, architects, analysts, project managers, and business stakeholders in an Agile environment.