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griddable.io · 3 hours ago

Principal Data Modeler

San Francisco, CA

Full-time

Hybrid

Lead/Staff

10+ years exp

Griddable.io is a company that focuses on AI-driven customer success through innovative solutions. They are seeking a Principal Data Modeler to design and manage the data models for their Marketing Data Warehouse, ensuring optimal performance and scalability while supporting data-driven decisions across Marketing.

AnalyticsBig DataCloud Data ServicesData IntegrationInformation TechnologySaaSSoftware

Responsibilities

Design and implement a robust data model that integrates data from core B2B systems, including Snowflake, Salesforce Data 360, multiple Salesforce orgs, Informatica MDM, and Amazon data lakes

Design and evolve scalable end-to-end data architecture; define standards for data modeling, ingestion framework, pipelines, data quality, etc

Architect tables and views to clearly define and calculate critical metrics (e.g., lead conversion, MQL, marketing driven pipe, ROI)

Translate business needs for marketing performance measurement, customer segmentation, targeting, and personalization into precise data requirements and model designs.Translate functional and non-functional requirements (e.g., analytical performance, query latency, automation throughput) into optimal logical, conceptual, and physical data model designs

Partner with Data Engineering to design data models that leverage advanced Snowflake features (e.g., clustering keys, materialized views, micro-partitions, time travel) to optimize query performance and cost efficiency

Master the benefits and trade-offs of modeling on each platform, such as leveraging Snowflake's zero-copy data sharing vs. federating queries to S3

Enforce rigorous data cataloging and metadata standards to ensure all marketing metrics have a single, unambiguous definition across the organization

Collaborate with other Data and Application Architects to ensure the data warehouse model aligns with the overall enterprise data strategy and upstream/downstream system architectures

Ensure the data model is intuitive and accessible for all Data Scientists, Analysts, Data and BI Engineers who build curated datasets, predictive models, and dashboards to measure and optimize marketing performance

Qualification

Data ModelingData ArchitectureSnowflakeSQLMaster Data ManagementETL ToolsMachine LearningData GovernanceCommunication SkillsOrganizational Skills

Required

Master's or Ph.D in Computer Science, Information Systems, or a related quantitative field

10+ years of hands-on data modeling, data architecture, or database design experience

5+ years of experience designing and implementing large-scale Enterprise Data Warehouses

Expert-level knowledge of dimensional modeling (Star/Snowflake schemas) and its application to business intelligence, reporting, and machine learning workloads including feature engineering for workloads such as attribution models, lead scoring, and propensity models

Extensive experience with marketing data domains (e.g., campaign management, CRM, web analytics, attribution/marketing mix modeling, propensity modeling, forecasting, and optimization). Demonstrated ability to model complex business processes, including slowly changing dimensions and historical data tracking

Proven, hands-on experience building and optimizing data models on a modern, cloud-native data warehouse platform, with deep expertise in Snowflake

Advanced proficiency with SQL and DDL/DML, especially optimized for the Snowflake ecosystem. Familiarity with ETL tools (e.g., dbt, Fivetran), cloud services (AWS, GCP, or Azure), and how to design data models that optimize their performance

Expert-level mastery of all major data modeling methodologies and implementation trade-offs between them such as 3NF (for applications), Data Vault (for integration layers), and Star/Snowflake schemas (for data science)

Deep experience modeling Master Data Management golden records and hierarchies, and integrating them with operational and analytical systems (e.g., Informatica MDM)

Experience implementing Data Mesh principles: domain ownership of data products, 'data as a product' mindset with clear SLAs and documentation, and federated governance that balances central standards with domain autonomy

Experience designing data models that support ML feature engineering, including feature stores and feature registries. Understanding of how data modeling decisions impact feature freshness, model training pipelines, and real-time inference

A proven track record of partnering directly with Data Engineering, Data Science, and Machine Learning Engineering teams to deliver data models that meet their specific needs. Must thrive in a high-velocity environment with rapid iteration cycles and be able to balance governance requirements with engineering agility

Experience partnering with Data Governance teams to ensure models are compliant, secure, and integrated with the enterprise data catalog

Exceptional communication skills. The ability to lead technical design discussions and articulate complex technical concepts and implementation trade-offs to both technical and business stakeholders

Highly organized and meticulous, with a passion for data accuracy and structural integrity

Preferred

Knowledge of Salesforce Data 360 platform with experience designing, deploying, and managing data model objects on enterprise deployments of Salesforce Data 360 is highly desirable

Deep understanding of the data modeling challenges within a multi-org Salesforce CRM environment and a customer activation platform (Salesforce Data Cloud canonical model DLO/DMO)

Company

griddable.io

Griddable.io is a San Jose, CA based SaaS startup that closed Series A funding in 2017 from August Capital, Artiman Ventures, and Carsten Thoma, founding CEO of Hybris (acquired by SAP).

Founded in 2016

San Jose, California, USA

11-50 employees