Top Data Engineer Jobs in Quezon City

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.

Designs and maintains data solutions, pipelines, and ETL processes for large-scale data generation, collection, processing, migration, and deployment. Ensures data quality, integrity, governance, and compliance while supporting real-time and batch processing. The role also serves as a subject matter expert, contributes to cross-team decisions, mentors team members, coordinates delivery, and improves data workflows.

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 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.

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.

Build and operate scalable financial market data pipelines for Binance stock products and AI applications. Responsibilities include sourcing, ingesting, modeling, processing, storing, and serving real-time and historical data; designing multi-market data frameworks; optimizing Flink pipelines; and establishing quality, reconciliation, monitoring, lineage, and recovery systems. The role also evaluates vendors and data sources, defines data contracts, and collaborates with trading, product, AI, legal, and compliance teams.

Designs and develops data platform blueprints and components using Palantir Foundry. Collaborates with integration and data architects to ensure cohesive system integration and consistent data models. Acts as a subject matter expert, supports team decisions, resolves technical challenges, improves platform processes, and mentors team members. The role requires expertise in Palantir Foundry, data platform architecture, data integration, governance, and cross-functional collaboration.

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.

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 maintains data pipelines, ETL processes, data models, SQL queries, Power BI dashboards, and reporting solutions. Applies data structures, algorithms, version control, CI/CD, and AI-assisted tools to create reliable, scalable analytics systems. Collaborates with business stakeholders to translate reporting needs into effective data solutions and may contribute to data warehouse design, database administration, and solution architecture.

Design, implement, and operate reliable integrations and ELT/ETL pipelines across enterprise applications, APIs, databases, files, SaaS platforms, and partner systems. Build Snowflake ingestion and transformation processes using Medallion Architecture, SQL, and Python. Implement scalable batch and incremental loading, data quality controls, monitoring, error handling, documentation, and lineage. Partner with analytics teams to deliver trusted, governed, and testable data pipelines.

Build full-stack tools, backend services, APIs, dashboards, and data workflows for assessing and improving AI training data and evaluations. Partner with quality and research teams to inspect agent trajectories, validate graders, measure quality, investigate workflow issues, and turn internal prototypes into reliable production platform capabilities.

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.

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.

Build, automate, and operate enterprise-scale data platforms using Databricks, Snowflake, Microsoft Fabric, Kubernetes, and cloud technologies. Responsibilities include Infrastructure as Code, CI/CD, GitOps, platform provisioning, reliability engineering, observability, security, governance, incident remediation, performance optimization, and FinOps. The role supports multi-environment deployments and self-service capabilities for data engineering and analytics teams.

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.