Top Data Engineer Jobs in Davao City

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.

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.

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.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Designs, develops, and maintains secure enterprise data pipelines and ETL/ELT processes integrating government and commercial systems. Ensures data quality, performance, availability, and real-time delivery for logistics, mission support, analytics, and operational decision-making. Responsibilities include database and pipeline optimization, cloud migration support, technical documentation, cybersecurity and governance compliance, troubleshooting, stakeholder coordination, operational exercise support, and mentoring junior personnel.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture; integrate multiple data sources; ensure data quality, governance, security, and performance; monitor pipelines; support BI, analytics, and ML; document models and workflows; collaborate with stakeholders.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Monitor daily data intake across channels, perform QA to detect inconsistencies or unusual trends, troubleshoot ingestion issues, document findings, and propose process/product improvements while working scheduled shifts.

Lead design and optimization of scalable ETL/data pipelines across on-premise and cloud environments, implement Microsoft Fabric solutions, build Power BI reports and unified data models, manage integrations and migrations (including Dynamics/Dataverse), ensure data quality/security/governance, troubleshoot and automate workflows, engage customers for requirements and delivery, and mentor team members.

Lead design, implementation and optimization of data infrastructure and scalable ETL pipelines across Microsoft Fabric, on-premise SQL, and Dynamics 365. Ensure data quality, governance, security, and unified data models. Build Power BI dashboards and analytical models, manage migrations and integrations, troubleshoot complex issues, document solutions, mentor team members, and drive platform adoption and best practices.

Hands-on senior data engineer role building and operating client-specific ELT pipelines and three-layer dbt projects on cloud warehouses (pref. BigQuery). Own pipeline operations, reconciliation and testing, semantic layer/metric definitions (Cube.dev), freshness/anomaly monitoring, PII-aware quality frameworks, and translate client domain discovery into reliable business marts to power dashboards and AI queries.