Top Data Engineer Jobs in Caloocan

Define, classify, and publish critical business data; ensure accuracy, accessibility, and security across the data lifecycle. Lead data product delivery to the data lake, enforce data quality and protection controls, collaborate with business and technology partners to remediate issues, and drive automation and governance improvements.

Design, build, and maintain data pipelines and databases to support actuarial analytics. Perform data manipulation with SQL/Python, ensure data quality, document sources, automate ingestion and transformations, and collaborate across teams to optimize data architecture and processes.

Build and maintain data pipelines and databases to enable analytics-driven decision making. Develop ETL/ELT processes using SQL and Python, design dimensional models, automate ingestion and orchestration (Airflow), evaluate data quality, document sources, and collaborate cross-functionally to improve processes and support actuarial analytics.

Design, build, and maintain end-to-end analytics and reporting solutions: ingest, transform, model, and store data; build Cognos and Power BI reports; ensure data quality; analyze datasets and present insights to stakeholders.

Design, build, and maintain Microsoft Fabric data architectures and ETL pipelines (Fabric, Synapse, ADF). Transform and optimize data for analytics and AI, support RAG/LLM workloads, ensure data quality, governance, performance, and document workflows.

Responsible for developing and maintaining ELT snowflake solutions, collaborating with teams, mentoring developers, and improving data engineering practices.

Data Engineer II will execute data project initiatives, maintain data applications, automate processes, and mentor junior developers while collaborating with teams.

Design, build, and maintain scalable ETL and real-time data processing systems on Databricks and AWS. Develop Spark-based pipelines, ensure data quality and governance, implement security controls, automate infrastructure with Terraform, and collaborate with analysts and engineers to resolve issues and optimize performance.

Design, build, test and maintain ETL pipelines using Python and Jupyter notebooks. Transform vendor and client data, migrate Excel logic to Python, document processes, respond to incidents within SLAs, automate processes, and collaborate with business and technical teams to improve data workflows.

Design, build, and maintain scalable Azure-based data pipelines, ETL/ELT processes, data warehouses, and reusable data services. Provide Level 2/3 support for data platforms, troubleshoot production incidents and data quality issues, implement CI/CD and DevOps automation, and collaborate with business and engineering teams to deliver governed analytics solutions.

The Data Engineer Consultant captures, transforms, and loads data using Azure services, ensuring data quality and developing ETL pipelines while collaborating with stakeholders.

Build and maintain cloud-based data pipelines and architectures using SQL and Python. Collaborate with modelers to assemble large datasets, automate and optimize ETL processes, ensure data quality, document technical solutions, and explore new data sources to support predictive analytics.

The Data Engineer will design, develop, and maintain data pipelines and architecture, ensuring data quality and governance, while collaborating with data professionals.

Design, develop, and maintain complex T-SQL stored procedures and SSIS ETL packages; performance tune databases; perform data analysis, validation, and testing; support production data loads and troubleshoot database issues while collaborating with technical and business teams.

Design, build and maintain scalable data ingestion and transformation pipelines (Snowflake, SnowPipe, dbt). Deliver ETL/data integration, cleansing scripts, documentation, and meet customer SLAs while following NRI ANZ methodologies and collaborating with clients and third parties.

Design, build, and maintain data models, analytics pipelines, and statistical models; perform EDA and validate analyses; ensure data quality and governance; produce visualizations and reports; collaborate with analysts, engineers, and stakeholders to deliver scalable, production-ready data solutions.

Lead design and delivery of analytics, automation, and AI-enabled data solutions to improve operational efficiency across Asia Pacific. Build data pipelines, models, dashboards, desktop apps and Excel add-ins; advise stakeholders, manage projects, establish standards, and coach junior team members.

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.

Design, build, test, tune, and support production ETL/data pipelines using PySpark, Python, advanced SQL, and AWS data services. Implement CDC, incremental loads, idempotent processing, reconciliation, data quality checks, and maintain documentation, tests, and release support while collaborating with analysts, architects, QA, DevOps, and senior engineers.

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.

Lead applications business development, account management, and service delivery for large accounts. Develop operational plans and budgets, resolve staffing and client issues, grow accounts through strategy and solutions, hire and coach staff, ensure contract compliance and client satisfaction, and collaborate on delivery models and financial targets.

The Data Engineer will analyze raw data, build data systems, enhance data quality, develop tools, and meet business objectives.

The Data Engineer will analyze raw data, build data systems, enhance data quality, evaluate business needs, and develop analytical tools.

The Data Engineer will analyze and organize raw data, build data systems and pipelines, and improve data quality for business needs.

The Data Engineer will analyze raw data, build data pipelines, evaluate business needs, enhance data quality, and develop analytical tools.