Mural - Engineering Manager, Data Modeling
Requirements
• 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 • Experience supporting data used for AI/ML model training • Background working on a data infrastructure or data platform team • The base salary for this role ranges from $120,000 - $150,000 + bonus + benefits. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. • Equal Opportunity • We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Responsibilities
• 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
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