Top Data Engineer Jobs in Davao City

Design, build, deploy, and maintain data processing applications and infrastructure; perform ETL, monitor production, resolve incidents, support stakeholders, and create proofs-of-concept and documentation.

The Data Engineer will design, develop, and maintain data pipelines, ensuring accurate data for reporting and decision-making, while improving internal processes and keeping up with technology trends.

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.

As a Data Engineer, you will analyze information needs, design and deploy data processing applications, maintain infrastructure, and resolve incidents in production while supporting internal staff and stakeholders.

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.

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.

Lead site-based CSA activities for data centre projects: manage contractors, ensure compliance with design, safety and quality, coordinate stakeholders, produce reports, and support project leadership.

Own quality for the Data Platform and Analytics products by validating pipelines, warehouse models, and reports. Write SQL to reconcile reports with raw sources and upstream APIs, build automated data quality checks (Soda), test Airflow DAGs and Databricks jobs, develop API tests for REST/GraphQL, and collaborate with Engineering, Product, and Analytics to document findings and prevent defects.

Support and optimize large-scale Spark/YARN jobs and storage (S3/EMR). Build job-profiling, cost-analysis, AI-assisted automation, monitoring/alerting (Prometheus, Flink/Spark), and internal ops tooling to improve stability and reduce costs.

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.