About this role.
Mural is seeking a hands-on Engineering Manager to lead a high-leverage Data Modeling team within its Data Organization. The role owns reusable core data models, shared metrics, semantic definitions, and reliable data products that support analytics, embedded customer insights, and AI/ML training. The manager will combine people leadership with deep technical work in SQL, Python, Spark, Databricks, and Airflow/Astronomer. Success depends on raising trust, availability, quality, and cross-functional reuse of company-wide data foundations. This Canada-remote position requires an experienced leader who can balance pragmatic delivery with durable architecture and governance.
Role DNA
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Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
5/5Salary analysis
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Core skills
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Sample interview questions
I would start by aligning Product, Analytics, and ML stakeholders on stable business entities, event grain, identifiers, and canonical definitions. I would build well-documented fact and dimension models with tested transformations, clear ownership, versioning practices, and semantic-layer metrics. I would then validate adoption through representative consumer use cases while avoiding consumer-specific logic in the foundational layer.
I establish a single source of truth by documenting definitions, lineage, owners, calculation logic, and intended use for each critical metric. I pair this with automated data-quality tests, freshness monitoring, reconciliation against trusted sources, and a structured process for approving changes. Regular stakeholder reviews help surface conflicting interpretations before they become embedded in downstream reporting.
I assess whether a request represents a repeatable business concept or a one-off consumption need. For repeatable concepts, I prioritize extending the shared model or semantic layer; for truly isolated needs, I enable an appropriate downstream solution without polluting the core layer. I communicate trade-offs explicitly using impact, reuse potential, risk, delivery effort, and operational cost.
I would define service expectations for critical datasets, including freshness, completeness, accuracy, and recovery objectives. Using orchestration and observability, I would implement dependency-aware workflows, automated tests, alerts, incident runbooks, backfill procedures, and ownership escalation paths. I would also monitor compute performance and cost so reliability improvements remain sustainable.
I set clear outcomes, technical standards, and ownership boundaries, then coach team members through design reviews, prioritization, and feedback. I stay close to the work by contributing to architecture, reviewing critical implementations, and unblocking complex decisions rather than taking ownership away from engineers. This creates autonomy while ensuring that foundational data products remain coherent and dependable.
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 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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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