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VP, Data Modeler and Platform Architect

VP, Data Modeler and Platform Architect

Platinum Resource GroupSanta Ana, CA
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JOB DESCRIPTION

Our client is seeking a Data Platform Architect & Modeler to design, validate, and optimize data models that support their analytics, reporting, and operational data needs. This role will actively contribute to building and maintaining data models while ensuring they align with business requirements and best practices. The ideal candidate will have experience working in Snowflake, dbt, data observability, DataOps, and DevOps, supporting our cloud-based data strategy and modernization efforts. Performs all duties in accordance with the Company's policies and procedures, all state and federal laws and regulations, wherein the Company operates.

  • Data Modeling : Work with functional business owners to understand business process and data elements that tie to the process. Design conceptual models to showcase the entities / data elements and relationships. Design models in NF, Star schema and snowflake schema as appropriate. Design, build, and maintain conceptual, logical, and physical data models to support analytics, reporting, and operational workloads. Ensure dimensional and relational data models support data warehousing and self-service analytics. Develop and optimize data structures in Snowflake DB to ensure performance, scalability, and business alignment. Utilize dbt to build and manage transformations for clean, structured, and reusable data models. Conduct POCs to evaluate new tools, methodologies, and modeling techniques to improve performance, efficiency, and scalability Establish and enforce data standards, governance, and best practices for data across the organization. Ensure data models comply with regulatory, security, and compliance requirements.
  • Platform Architecture : Design and implement small-scale prototypes and evaluations to test approaches for data modeling, performance running, and architecture improvements. Assess and compare data modeling techniques, integration strategies, and observability tools to recommend the best solutions for the enterprise. Work closely with data engineering and analytics teams to assess new methodologies before full-scale implementation. Document findings from evaluations and provide technical guidance on their adoption. Implement data observability tools to monitor data health, lineage, and anomalies across pipelines. Integrate DataOps principles to automate data quality checks, validation, and governance processes. Work with DevOps teams to ensure CI / CD pipelines support automated deployments and version control for data models. Define and enforce data validation, monitoring, and alerting mechanisms to proactively address issues before they impact stakeholders. Partner with teams to identify and resolve data inconsistencies, drift, and performance bottlenecks. Partner with business teams to understand reporting and analytics needs, translating them into scalable data models. Work with data engineers, BI teams, and application developers to optimize data structures for various use cases. Collaborate with data governance teams to define metadata, lineage, and data quality standards. Provide technical guidance on data modeling, DataOps, and best practices for data architecture. Evaluate and recommend modern data modeling tools and methodologies to improve efficiency and scalability. Stay up to date on industry trends and cloud data technologies to enhance data architecture. Support the data foundation initiative, contributing to the modernization of enterprise data platforms.
  • Treat people with respect; keep commitments; inspire the trust of others; work ethically and with integrity; uphold organizational values; accept responsibility for own actions.
  • Demonstrates knowledge of and adherence to EEO policy; shows respect and sensitivity for cultural differences; educates others on the value of diversity; promotes working environment free of harassment of any type; builds a diverse workforce and supports affirmative action.
  • Follows policies and procedures; completes tasks correctly and on time; supports the company's goals and values.
  • Performs the position safely, without endangering the health or safety to themselves or others and will be expected to report potentially unsafe conditions. The employee shall comply with occupational safety and health standards and all rules, regulations and orders issued pursuant to the OSHA Act of , which are applicable to one's own actions and conduct.

QUALIFICATIONS

  • years of experience in data modeling, platform architecture, or database design.
  • years of experience in banking and financial services (commercial banking preferred).
  • Experience with data modeling tools (ER Studio, Erwin, or similar).
  • Experience on creating conceptual, logical and physical data models using various normal forms, star and snowflake schema.
  • Expertise in dimensional (OLAP) and relational (OLTP) data modeling techniques.
  • Proficiency in SQL and experience optimizing queries for performance and scalability.
  • Hands-on experience with Snowflake, dbt, and data observability tools.
  • Experience implementing DataOps and DevOps practices in a data environment.
  • Strong understanding of data governance, data quality, and metadata management.
  • Strong problem-solving skills and ability to work independently on technical implementations.
  • Excellent communication skills to collaborate with technical and non-technical stakeholders.
  • Experience with ETL / ELT frameworks and data pipeline best practices.
  • Knowledge of Python, Spark, or other data transformation tools.
  • Must be able to prioritize multiple projects and objectives in rapidly changing environment.
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