Agent Based Modeling Scientist Jobs in Minnesota

Position Summary

Analytics Engineer is responsible for designing, building, and optimizing a modern manufacturing data platform that transforms complex operational data into trusted, analytics-ready solutions. The position combines hands-on data engineering with technical leadership, leveraging Snowflake, DBT, Matillion, and CI/CD best practices to develop scalable, high-performing data pipelines and models. Working closely with business stakeholders and product owners, the individual will ensure data quality, governance, and observability while building semantic data layers that support reporting, self-service analytics, and future AI-driven capabilities. The ideal candidate brings deep expertise in modern data architecture, strong communication skills, and experience delivering scalable data products that enable business insights and operational excellence.

Stack: Snowflake, Snowflake AI/Cortex, DBT, Matillion, Dimensional Modeling, Data Vault 2.0, GitHub/CI-CD, Power BI

Essential Job Functions                                                      

• Design, develop, and maintain scalable data ingestion and orchestration processes using Matillion or similar enterprise ETL/ELT tools to integrate data from complex manufacturing systems into Snowflake.
• Build, deploy, and support end-to-end data transformation pipelines using DBT and Snowflake, moving data through Bronze (raw), Silver (integrated), and Gold (analytics-ready) layers.
• Develop and maintain Data Vault 2.0 models and related data architecture standards to ensure data is auditable, scalable, and adaptable to evolving business systems and ERP environments.
•  Create and optimize dimensional models, star schemas, and semantic data layers that support self-service analytics and high-performance reporting in Power BI and other analytical tools.
•  Design, implement, and manage CI/CD processes, source control standards, and automated deployment pipelines using GitHub Actions, Azure DevOps, or similar technologies to ensure reliable and repeatable releases.
•  Establish and maintain monitoring, logging, alerting, and observability capabilities to proactively identify, troubleshoot, and resolve data pipeline and platform issues.
• Implement and maintain automated data quality controls, validation testing, and observability frameworks to ensure the accuracy, completeness, and reliability of enterprise data assets.
•  Partner with Data Product Owners, business stakeholders, and cross-functional teams to evaluate technical requirements, assess solution feasibility, and translate business needs into actionable technical deliverables.
•  Provide technical leadership and guidance on data platform architecture, development standards, best practices, and documentation to ensure scalable and maintainable solutions.
• Define and promote architectural patterns that support future AI, machine learning, and advanced analytics capabilities within the Snowflake ecosystem, including semantic layers, secure data access, search, and agent-based workflows.
• Analyze and optimize Snowflake compute utilization, data processing performance, and SQL query execution to improve platform efficiency, scalability, and end-user experience.
• Collaborate effectively across technical and business teams, communicating complex concepts clearly and contributing to the successful delivery of enterprise data and analytics initiatives.

Minimum Requirements, Education & Experience (incl. KSA's and certifications)

• Bachelor's degree in Computer Science, Engineering, or a related field
• 6 years of data engineering and/or analytics engineering experience with demonstrated expertise in Snowflake and DBT.
• Experience querying and consuming data from Microsoft SQL Server (MSSQL) and REST APIs.
• Understanding of data connectivity methods, including ODBC, ADO, and JDBC. Experience with PostgreSQL, MySQL, or MariaDB is a plus.
• Expert experience using Git-based source control workflows and building automated CI/CD deployment pipelines for data platforms.
• Proven experience designing and implementing Medallion/Lakehouse data architectures and dimensional data models.
• Working knowledge and experience with Data Vault 2.0 architecture is preferred.
• Demonstrated ability to communicate effectively with stakeholders at all levels of the organization and collaborate successfully across cross-functional teams.
• Proven ability to gather requirements, translate business needs into technical solutions, and work effectively with diverse team members and stakeholders.

Desirable Criteria & Qualifications

• Experience working with manufacturing data domains, including Bills of Materials (BOMs), Inventory, Sales and Work Orders, Supply Chain, Quality (NCRs/CAPA), Labor and Scrap Reporting, Machine Usage, and Efficiency Metrics.
• Familiarity with machine interfaces and streaming data ingestion technologies.
• Hands-on experience with Snowflake performance tuning, governance, role-based access controls, and platform capabilities that support scalable analytics and AI-ready data products.
• Familiarity with Snowflake AI capabilities, including Cortex AI (CoCo), Cortex Analyst, Cortex Search, Snowflake CoWork, Snowpark, Agents, or related features that support governed AI/ML use cases within the data platform.
• Experience preparing governed, well-modeled data products for AI/ML and agentic use cases, including metadata management, semantic descriptions, access controls, and business-friendly data definitions.
• Experience with analytics visualization tools, such as Power BI, and an understanding of how downstream consumers interact with data products.
• Ability to write custom Python scripts to support integrations when out-of-the-box tools do not meet business or technical requirements.

#LI-MH1

This pay range reflects the base hourly rate or annual salary for positions within this job grade, based on our market-based pay structures. Actual compensation will depend on factors such as skills, relevant experience, education, internal equity, business needs, and local market conditions. While the full hiring range is shared for transparency, offers are rarely made at the minimum or maximum of the range

All Employees:

Our 401k retirement savings plan with a company match contribution; onsite health clinics, discretionary holiday bonus program (based on years of service), Cretex University, 24/7 employee assistance program with access to five confidential visits with a licensed counselor at no cost, wellness program with incentives, an employee death benefit, and employee sick and safe leave are available to all Cretex employees. 

20+hours:

Cretex's medical benefit package includes: comprehensive medical insurance with access to virtual providers; dental insurance (Little Partners Dental benefit covers services 100 percent for children 12 and younger when seen by a Health Partners in network provider); vision insurance; a pre-tax health savings account, healthcare and dependent care pre-tax reimbursement accounts; paid holidays, paid time off; and our discretionary profit sharing program are available to employees working 20+ hours/week. 

30+ hours:

Parental Leave, accident and critical illness benefits, optional employee, spouse, and child life; short and long term disability; company provided life insurance; and tuition assistance programs are available to employees working 30+ hours per week. 

(Some benefits are subject to eligibility criteria.)

Applicants will receive consideration for employment regardless of their race, color, creed, religion, national origin, sex, sexual orientation, gender identity, disability, age, veteran status, marital status, family status, status with regard to public assistance, or any other protected status as required by law.  

Our company uses E-Verify to confirm the employment and eligibility of all newly hired employees. To learn more about E-Verify, including your rights and responsibilities, please visit www.dhs.gov/E-Verify.