Data Modeler - Data Lakehouse at Pointwest

Quezon City, Metro Manila, Philippines -

Full Time

Start Date

Immediate

Expiry Date

24 Jun, 26

Salary

0.0

Posted On

26 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Modeling, Data Lakehouse, Data Warehousing, Conceptual Modeling, Logical Modeling, Physical Modeling, SQL Analytics, Data Science, Medallion Architecture, Dimensional Modeling, Star Schema, 3NF, Data Vault, Snowflake, Databricks, BigQuery

Industry

IT Services and IT Consulting

Description

Data Modeler Pointwest is looking for a visionary Data Modeler to serve as the technical lead for our modern data initiatives. This is a role for a high-impact practitioner who has successfully navigated the transition from traditional Data Warehousing to modern Data Lakehouse architectures. Driven by our core values of Leadership, Excellence, and Innovation, you will design the blueprints that transform raw data into a strategic corporate asset. You will embody the Pointwest culture of Agility, Accountability, and Customer Centricity by ensuring the structural health of our data ecosystem and optimizing cloud performance to deliver world-class insights. Key Result Areas and Key Performance Indicators A. Key Tasks Delighting Our Customers & Stakeholders Architecting Excellence: Design and implement conceptual, logical, and physical data models that support both structured SQL analytics and advanced Data Science workloads. Medallion Strategy: Lead the implementation of Bronze, Silver, and Gold data layers to provide stakeholders with curated, high-quality data assets. Reliability First: Act as the first line of defense against "Garbage In, Garbage Out" by designing automated validation scripts and performing root cause analysis on discrepancies. Growing Our Business Schema Evolution: Manage the lifecycle of data schemas to ensure the environment remains scalable and flexible as the business integrates new data sources. Technical Leadership: Serve as the bridge between complex business logic and technical execution, ensuring all data movements are documented and transparent. Improving the Way We Work Performance Optimization: Collaborate with engineers to optimize partitioning, clustering, and indexing (e.g., Z-Ordering) to reduce cloud compute costs and improve speed. Standardization: Enforce enterprise data standards and naming conventions to prevent the creation of "Data Swamps" and ensure a unified logical model. Developing Myself and Others Multi-Model Expertise: Apply and share knowledge of various modeling techniques, including Dimensional/Star Schema, 3NF, and Data Vault, to solve diverse business challenges. B. Key Metrics Delighting Our Customers & Stakeholders Data Trust Score: Maintain a high "Data Trust Score" (>95%) through successful automated validation and rapid resolution of discrepancy tickets. Model Adoption: Target 80% or more of production reports being sourced from curated "Gold Layer" models rather than ad-hoc tables. Growing Our Business Architectural Integrity: Measurable reduction in "Schema Debt" by minimizing deprecated or redundant tables over time. Mapping Accuracy: Achieve a "Logic Re-work" rate of less than 10% during the development phase. Improving the Way We Work Compute Efficiency: Deliver a 15-20% reduction in cloud compute costs (Snowflake/Databricks credits) through smart physical design and refactoring. Documentation Coverage: Maintain 100% documentation coverage for Gold-layer assets, including full Source-to-Target Mappings (STTM). Developing Myself and Others Deployment Velocity: Ensure Data Engineers can move from model design to production with minimal clarification sessions, reflecting the clarity of your technical guidance. Qualifications Experience: 5-7 years of dedicated Data Modeling experience within Data Warehouse or Lakehouse environments. Proven Track Record: Successfully delivered at least 5 distinct data projects from initial requirements through to production. Technical Mastery: Expert-level proficiency in Kimball/Star Schema and 3NF; hands-on experience with Medallion Architecture. Cloud Platforms: Technical proficiency in Snowflake, Databricks, or Google BigQuery. Tooling: Experience with industry-standard tools such as erwin, SQLDBM, SAP PowerDesigner, or dbt. Education: Bachelor’s degree in Computer Science, Information Systems, or a related quantitative field.

Responsibilities

The Data Modeler will serve as the technical lead, designing conceptual, logical, and physical data models supporting both SQL analytics and Data Science workloads, while leading the implementation of Bronze, Silver, and Gold data layers (Medallion Strategy). This role involves optimizing performance through partitioning and indexing, enforcing enterprise data standards, and acting as the bridge between business logic and technical execution.