Top Data Engineer Jobs in the Philippines

Design, develop, and support data and application solutions for regulatory reporting across Asian markets. Build and optimize SQL Server, T-SQL, SSIS, SSRS, and ETL components; manage Azure DevOps CI/CD pipelines; translate regulatory requirements into scalable solutions; maintain security, risk, audit, and governance standards; troubleshoot performance issues; document systems; and coach team members.

Build and maintain end-to-end commercial and operational data pipelines, analytics models, dashboards, scheduled reports, and self-service data products. Own data quality, testing, documentation, orchestration, deployment, monitoring, and light infrastructure work. Partner with data science, DevOps, and cross-functional teams to deliver trusted insights for Sales, Marketing, Finance, Product, and Strategy.

Develop and maintain scalable data pipelines, transformation logic, and Snowflake data models. Use Airflow, dbt, SQL, and Python to deliver reliable data solutions, implement quality monitoring, optimize performance and costs, and support modern data infrastructure. Collaborate with infrastructure, analytics, and business teams, document processes, participate in code reviews, and apply data engineering, testing, governance, and CI/CD best practices.

Develop and maintain scalable data pipelines, transformation logic, and data models using Airflow, dbt, Snowflake, SQL, and Python. Ensure data quality through testing, monitoring, alerting, and documentation. Collaborate with infrastructure, analytics, and business teams to optimize data platforms, resource utilization, deployment practices, and business solutions. Participate in code reviews and promote data engineering best practices.

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.

Leads hands-on engineering for modernization of an enterprise eDiscovery data platform. Designs and codes Java services, Oracle PL/SQL, Python utilities, APIs, pipelines, and integrations across on-premise and GCP environments. Owns legacy decomposition, data modeling, ingestion, reconciliation, auditability, observability, performance, security, testing, and controlled migration. Influences technical direction through implementation, code reviews, design forums, mentoring, and production support while ensuring regulated-enterprise requirements are met.

Translate business needs into data requirements, mappings, user stories, and acceptance criteria. Use SQL and Excel to profile, reconcile, and validate data. Partner with engineers and architects on data lake solutions, quality controls, testing, and production readiness. Develop dashboards and analytical outputs, while aligning data definitions, ownership, lineage, and quality expectations across global markets.

Owns ingestion, data modeling, reconciliation, and lineage for a financial billing platform on GCP. Designs auditable, idempotent BigQuery pipelines; implements data quality, validation, quarantine, and reconciliation controls; handles monetary values accurately; and develops tested Python and advanced SQL. Collaborates with engineering, analytics, and finance stakeholders while mentoring team members and improving data engineering standards.

Provides hands-on technical leadership for secure, scalable data-ingestion platforms supporting Risk, Finance, and Treasury. Responsibilities include architecture, Java and Spark development, cloud and containerized delivery, reusable frameworks, CI/CD, testing, observability, resilience, production support, security controls, and technical direction. The role also mentors engineers, influences cross-team architecture, manages technical debt and operational risk, and partners with business and control stakeholders in a regulated financial-services environment.

Develops database queries, data analyses, Python and SQL scripts, data pipelines, ETL processes, APIs, relational schemas, and cloud data solutions. Builds dashboards and embedded reports using Power BI or Qlik, integrates data from diverse sources, and improves technology-enabled operational processes. The role supports loan services and requires strong financial services, database, business intelligence, cloud, and data warehousing expertise.

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.

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, 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, develops, tests, troubleshoots, and deploys cloud-based data and analytics solutions using AWS, infrastructure as code, and software development practices. The role involves data integration, automation, application and data model design, system integration, code reviews, testing, troubleshooting, and stakeholder collaboration. It also provides informal leadership through mentoring, design sessions, coding sessions, and communication of complex technical concepts.

Own the design, development, optimization, and reliability of large-scale data pipelines across AWS and Hadoop environments. Lead technical design, workflow orchestration, data quality, governance, monitoring, documentation, and production support. Mentor a small engineering team, collaborate with cross-functional stakeholders, and apply AI-assisted automation. Required expertise includes SQL, Python, Hive, AWS services, Airflow, and data engineering operations, with Spark and Snowflake experience preferred.

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.

Own and scale machine learning data infrastructure for advertising systems, including high-throughput feature pipelines, real-time streaming, embeddings, feature serving, and deep learning model training pipelines. Improve latency, reliability, throughput, monitoring, testing, and CI/CD while partnering with data science, modeling, product, and infrastructure teams. The role involves designing resilient distributed systems and delivering measurable improvements to model performance and platform scalability.

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.