Engineering Manager, Data Modeling

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Motivo actual: Descartada: seniority alto en el título ("Manager").

Descripción

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 and systems Partner across teams: Work closely with Product to define foundational product concepts, with Data Platform on architecture and reliability, and with Business Analytics, Data Science, ML, and in-product insights teams as key consumers Set technical direction: Make pragmatic decisions about modeling standards, architecture, orchestration (Airflow/Astronomer), and tooling that balance near-term delivery with long-term maintainability WHAT YOU'LL BRING Leadership experience: 2+ years leading or formally managing data professionals Strong foundational data modeling experience: 6+ years building and owning shared, reusable core data models in modern data platforms Experience supporting SaaS businesses: Familiarity with product usage, and customer data common to SaaS environments Hands-on technical depth: Advanced SQL, strong Python, experience with Spark, and comfort working in a modern lakehouse environment (e.g., Databricks) Operational mindset: Experience designing for data quality, reliability, and availability, including workflow orchestration with Airflow or Astronomer Reuse-first thinking: Proven ability to build foundational models and metric definitions that support multiple use cases rather than one-off data marts Systems awareness: Understanding of performance, cost, governance, security, and compliance considerations for shared data Collaborative approach: Ability to partner effectively with Product, Data Platform, Business Analytics, Engineering, and Data Science teams Pragmatic delivery mindset: You know when to ship and when to invest in durability Interest in AI-assisted development: You actively explore AI tools to improve how you and your team build and maintain data models NICE TO HAVE Experience supporting data used for AI/ML model training Background working on a data infrastructure or data platform team Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Equal Opportuni

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