$ cat jobs/engineering-manager-data-modeling-mural-c0f1458bebf2.json
Engineering Manager, Data Modeling
ABOUT THE TEAM The Data Modeling team builds and maintains the core data models and metrics that power decision-making across Mural. We are part of the Data Organization and focus on creating shared, reusable data models that represent key product and business concepts and are used across the company. Our work supports internal analytics, customer insight reports embedded in the product, and AI/ML model training. We partner closely with Product, Engineering, Data Platform, Business Analytics, Data Science, and Analytics Engineering to ensure the company is working from consistent definitions, high data quality, and reliable data availability. We are a small, high-leverage team focused on building durable data foundations rather than one-off solutions. YOUR MISSION You will own the delivery and evolution of Mural’s core data models and shared metrics, with a strong focus on data quality, reliability, and availability. This is a hands-on leadership role. You will not build stakeholder-specific data marts or ad-hoc analyses. Instead, you will focus on building foundational, reusable data models and metric definitions that support many use cases across the company. Your success will be measured by how widely trusted, consistently available, and broadly reused the data models and metrics you own are across teams such as Business Analytics, in-product insights, and ML. WHAT YOU'LL DO Own and evolve core data models and metrics: Define and maintain shared models for product usage, customers, accounts, and key business metrics that support analytics, in-product customer insights, and AI/ML model training Build and operate foundational data products: Stay hands-on building models using SQL, Python, and Spark in a modern lakehouse environment (e.g., Databricks), with strong attention to data quality, availability, performance, and cost Define shared semantics: Design and maintain shared metric definitions and semantic layers so data is interpreted consistently across teams an
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