Top Data Engineer Jobs in the Philippines

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.

Design and maintain Azure-based data pipelines, ingestion workflows, layered lakehouse transformations, dimensional models, and orchestration processes. Migrate legacy reporting logic, implement data quality testing, maintain documentation, support CI/CD through Azure DevOps, and collaborate with BI teams on curated datasets and report dependencies. Monitor platform performance and handle incremental loading, schema drift, retries, and change data capture.

Build and operate scalable financial market data infrastructure, including source evaluation, ingestion, cleansing, modeling, real-time and batch processing, quality governance, reconciliation, monitoring, and data services. Develop Flink-based pipelines supporting trading, research, and AI applications. Partner with product, trading, compliance, procurement, and engineering teams to define data contracts, source strategies, and consistent financial data semantics across markets.

Designs, develops, tests, deploys, and monitors data pipelines, ETL jobs, data models, and data warehouses. The role supports BI reporting by ensuring accurate, validated, and accessible datasets. Responsibilities include configuring integrations, automating production jobs, optimizing ETL and database performance, troubleshooting errors, maintaining documentation, and collaborating with technical users and business stakeholders to define data requirements and deliver analytics datasets.

Designs, develops, tests, deploys, and maintains data pipelines, ETL processes, data warehouses, and data models supporting business intelligence reporting. Responsibilities include configuring integrations, automating jobs, validating data, monitoring performance and errors, troubleshooting issues, optimizing ETL and database environments, and collaborating with technical users and business stakeholders to deliver accurate datasets and insights.

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.

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.

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.

Designs and supports data platform blueprints, data models, and integrated systems. Collaborates with integration and data architects to ensure data integrity and accessibility, optimizes data processes, troubleshoots data issues, and contributes to platform implementation. The role requires Python, LLMOps, prompt engineering, cloud data platform knowledge, and data integration expertise, along with mentorship of junior team members.

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.

Owns clinical data operations by building and maintaining ELT pipelines, warehouse models, data contracts, quality controls, reconciliations, dashboards, and monitoring. Integrates clinical, laboratory, sequencing, and corporate data into validated, audit-ready systems. Supports production reliability, partner data transfers, regulatory compliance, root-cause analysis, documentation, stakeholder collaboration, and technical guidance for research and development teams.

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.

Own a business function’s data domain by defining metrics, building governed BigQuery and Coalesce pipelines, creating authoritative models and trusted dashboards, and identifying anomalies and data gaps. Partner with Finance, Product-Led Growth, People, or Partnerships stakeholders, contribute to shared data platform standards, and automate repetitive workflows using Python, LLMs, and agentic systems.

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.

Develop predictive and prescriptive analytics, machine learning models, and data models for semiconductor manufacturing. Analyze production, quality, process, and equipment data to improve yield, productivity, and operational efficiency. Collaborate with manufacturing engineers and stakeholders, support smart manufacturing initiatives, and communicate insights to technical and business audiences.

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.

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 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.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

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.