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SUMMARY:
The Analytics Engineer is responsible for designing, building, and maintaining scalable analytics data models and business intelligence solutions that enable data-driven decision-making across the Companys vertically integrated operations, including cultivation, manufacturing, wholesale, retail, compliance, and supply chain. This role partners closely with Data Engineering and cross-functional stakeholders to translate complex business requirements into reliable, well-structured datasets, dashboards, and actionable insights, while helping establish and maintain standards of analytics, best practices, and scalable processes to support the continued growth and maturity of the Companys data function

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ESSENTIAL FUNCTIONS AND RESPONSIBILITIES (this list is not all inclusive)
Designs, develops, and maintains scalable analytics data models using dbt, supporting end-to-end workflows from staging to curated analytics marts.
Ensures data models are production-ready, reliable, well-documented, and optimized for reporting and downstream analytics use cases.
Partners with Data Engineering to build and maintain efficient, scalable, and sustainable data pipelines.
Translates business requirements into structured datasets, semantic layers, and actionable insights to support operational and strategic decision-making.
Develops, maintains, and enhances Tableau dashboards and reports using analytics-engineered data models.
Enables self-service analytics by delivering clean, trusted, and accessible datasets to business users.
Applies strong SQL and data modeling practices to ensure high-performance query execution and data integrity.
Utilizes Python for data processing, automation, and analysis where applicable.
Participates in version control processes using Git, including code reviews, documentation, and collaboration workflows.
Supports orchestration and scheduling of data workflows using tools such as Airflow or similar frameworks.
Establishes and promotes analytics engineering standards, including testing, documentation, and deployment best practices.
Collaborates with cross-functional teams to ensure alignment between data solutions and business objectives.
Works independently with minimal oversight, applying critical thinking and problem-solving skills to deliver high-quality outcomes.
Executes ad hoc projects as assigned by direct supervisor.

CORE COMPETENCIES
Demonstrates strong analytical, problem-solving, and critical-thinking skills with a high level of attention to detail. Ability to synthesize complex data sets, identify trends, and translate findings into actionable business insights. Approaches problems with structured thinking and develops scalable, data-driven solutions.
Takes full ownership of data models, dashboards, and deliverables, ensuring accuracy, reliability, and integrity. Proactively identifies risks, gaps, and inconsistencies, and implements solutions to mitigate impact to the business.
Demonstrates a strong understanding of how data supports operations across retail, cultivation, manufacturing, and supply chain. Aligns analytics solutions with business priorities, compliance requirements, and strategic objectives.
Works effectively across teams, building strong relationships with both technical and non-technical stakeholders. Demonstrates excellent verbal and written communication skills, with the ability to translate complex technical concepts into clear, actionable insights.
Communicates data insights in a clear, concise, and compelling manner. Translates complex analyses into meaningful narratives that support informed decision-making at all levels of the organization.
Demonstrates the ability to work independently, manage competing priorities, and deliver results in a fast-paced, evolving environment. Remains flexible and solution-oriented while continuously identifying opportunities to improve processes, tools, and data quality.
Exhibits a proactive mindset in exploring new tools, technologies, and methodologies. Challenges assumptions and introduces improvements that enhance analytics capabilities and operational efficiency.
Effectively prioritizes workload, manages multiple projects, and consistently delivers high-quality outputs within established timelines. Maintains accountability and a strong sense of urgency in achieving results.
Demonstrates the ability to be flexible and work effectively across various sectors of the department as needed or requested by a direct supervisor. This includes the capacity to support other departments, ensuring seamless collaboration and responsiveness to organizational needs.

PHYSICAL REQUIREMENTS
Constantly: Walking, sitting at a desk, grasping/gripping, bending/stooping/squatting, finger dexterity, computer input, coordination of hand and eye.
Frequently: Standing for long periods of time, climbing stairs, and twisting.
Occasionally: Reaching above shoulder height, lifting 20-30 lbs., ability to do push/pull motions.
Constantly: Use visual display terminals (e.g., computers, tablets) for extended periods of time.
Ability to work in a stressful, fast-paced environment
Occasionally: Travel to Company locations.

EDUCATION AND EXPERIENCE REQUIRED
Bachelors degree in Data Science, Computer Science, Information Systems, or a related field, or equivalent practical experience.
Advanced proficiency in SQL, with the ability to write efficient, scalable, and well-structured queries for analytics and data transformation.
Hands-on experience with modern cloud data warehouses (e.g., Snowflake, Redshift, BigQuery).
Experience designing, building, and maintaining dbt projects, including staging, transformation, and analytics layers.
Proven experience developing and maintaining Tableau dashboards and reporting solutions.
Experience using Python for data processing, automation, or analysis (e.g., pandas, numpy).
Familiarity with data orchestration frameworks such as Airflow and understanding of workflow scheduling and monitoring.
Experience using Git for version control, including collaboration and code review workflows.
Understanding of analytics engineering best practices, including testing, documentation, and deployment processes.
Exposure to CI/CD practices for data or analytics workflows, preferred.
Experience working within cloud-based environments (e.g., AWS, GCP) and familiarity with DevOps tools (e.g., Docker, Kubernetes) is a plus.
Experience working with complex data models and integrating multiple data sources across business domains.
Exposure to multi-domain analytics environments, including retail, supply chain, manufacturing, or regulated industries such as cannabis, is highly desirable.

Must be 21+ years old and pass a criminal background check requirement

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