Manager of Enterprise Analytics

Who We Are

Ascend Wellness Holdings (AWH) is a vertically integrated cannabis cultivator, processor, and retailer with assets in Illinois, Maryland, Massachusetts, Michigan, New Jersey, Ohio, and Pennsylvania. Our mission is to shape the future of cannabis as a leading healthcare solution while delivering a premium, customer‑focused retail experience. We are committed to building a high‑performing team that is passionate about our products, our communities, and creating operational excellence across our growing footprint.

Manager, Enterprise Analytics is responsible for the design, operation, and maintenance of AWH's SQL database environment, ETL/ELT pipelines, and the enterprise semantic models that serve as the authoritative source for Power BI reporting and analytics. The role requires deep SQL and Azure platform expertise, strong Power BI and Microsoft Fabric proficiency, and the ability to manage data projects end‑to‑end in a collaborative environment.

This is a remote position.

• Supports and maintains Azure‑based SQL Server environments across development, test, and production tiers.
• Participates in the architecture and design of the enterprise data platform, including Microsoft Dev Ops and Fabric.
• Acts as coordinator for data engineering projects to ensure appropriate design, performance, and security controls.
• Evaluates new data and BI technologies and assesses their application in the AWH environment.

• Designs, builds, and maintains enterprise semantic models (Power BI Tabular, Direct Lake) as the authoritative business logic layer for reporting.
• Defines and governs measures, KPIs, hierarchies, and relationships to ensure consistency across downstream consumers.
• Partners with business stakeholders to document metric definitions and encode them in reusable, version‑controlled model assets.
• Monitors and tunes semantic model performance including DAX optimization and incremental refresh configurations.

• Designs and develops ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric Pipelines, dbt, or equivalent tools.
• Develops and integrates data from a variety of sources including transactional systems, SaaS platforms, and third‑party APIs.
• Manages and monitors production data jobs and pipelines daily, ensuring SLA adherence and data freshness.
• Performs scheduled maintenance including code promotion to test and production environments.

• Manages mission‑critical databases requiring 24×7 business operational support.
• Responsible for SQL design reviews, query optimization, and data security enforcement.
• Implements data quality checks and observability tooling across the data platform.
• Develops and enforces data engineering best practices including documentation, naming standards, and CI/CD practices.

• Bachelor's Degree in Computer Science, Data Science, Information Systems, or a related field.
• Minimum five years of experience in BI engineering, data engineering, or a related SQL/database role.
• Demonstrated experience building and maintaining semantic models in Power BI (Tabular, DAX) or Azure Analysis Services.
• Hands‑on experience with Azure data services:
  Azure SQL Database, Azure Synapse Analytics, and/or Azure Data Factory.
• Working knowledge of Microsoft Fabric including Fabric Lake houses, One Lake, Fabric Pipelines, and Direct Lake models.
• Experience with dbt or similar transformation frameworks for building modular, tested data pipelines.
• Deep proficiency in SQL including query tuning, execution plan analysis, and optimization for large‑scale datasets.
• Experience with SQL Server in the Azure environment.
• Knowledge of database backup/restore strategies, transactional replication, and database security concepts.
• Familiarity with monitoring and observability tools for SQL Server and cloud data platforms (Azure Monitor or equivalent).
• Familiarity with Git and CI/CD practices applied to data assets and model deployments.
• Retail industry experience preferred, ideally within a multi‑site or high‑growth environment.
• Strong problem‑solving and…