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Data Modeler
4 weeks ago
InvestEd National Capital Region, Philippines Data Modeler We are looking for a Data Modeler to design, optimize, and maintain the data architecture that powers our warehouse and models. You will be accountable for creating and governing high-performance data models (such as star schemas or dimensional models), ensuring data quality and integrity, and enabling scalability, performance, and usability of the data infrastructure. In this role, you will directly own the strategy for data models and standards, while also advising and aligning with engineering and analytics teams to ensure consistency across pipelines, transformations, and reporting. Your work will be essential in supporting analytics and reporting functions, while keeping the data environment future-ready and business-focused. Responsibilities: Create and maintain data models (e.g., star schema, dimensional models) to support analytics, business intelligence, and reporting. Ensure data quality and integrity across systems by defining and enforcing standards and processes. Establish and govern modeling conventions, metadata, and documentation, ensuring consistency and usability. Collaborate with stakeholders to define data requirements, aligning data architecture with business needs. Provide architectural guidance on the design of data ingestion pipelines, ensuring they align with modeling standards. Advise on database infrastructure scaling strategies, setting expectations for performance and flexibility. Define modeling standards for data transformation jobs (e.g., dbt), ensuring efficiency and accuracy without direct execution. Ensure data models are structured appropriately to support BI dashboards, advising analytics teams on alignment with reporting needs. Review and advise on pipeline health and performance reports, guiding engineering teams on architectural improvements. Qualifications: At least 5 years of experience in data architecture, data modeling, or a similar role Strong expertise in SQL and advanced data modeling techniques (e.g., star schema, snowflake schema, dimensional models) Experience redesigning and optimizing existing data warehouses, not just building from scratch Hands‑on familiarity with structured and unstructured data processing Knowledge of ETL/ELT processes, cloud data platforms, and big data technologies Seniority level Mid-Senior level Employment type Full-time Job function Engineering and Information Technology Industries Financial Services and Data Infrastructure and Analytics #J-18808-Ljbffr
