Data Engineer

Title: Sr. Data Engineer (Lead) 
Duration: 6-12 month contract-to-hire 
Location: Hybrid---- Oak Park Heights, MN---3 days/week
Interviews: 2 rounds

Local (Minnesota) candidates only (NOT relocate), as the selected person will need to work on-site in their Oak Park Heights, MN office 3 days/week.  Oak Park Heights is near Stillwater.  This is on the east side of town near the Minneapolis-St. Paul metro area.

• Looking for a senior-level data engineer with 10-12 years of experience.
• Should be a true data engineer, not a data analyst, ML engineer, or data scientist.

Required Skills
• Snowflake experience (7 or 8 out of 10).
• DBT experience (at least 5 out of 10).
• "I want somebody with an engineering mindset, not a tool specialist."

Responsibilities
• Work with solution architects and the principal to understand patterns.
• Be able to suggest different patterns if needed.
• Work with product owner/delivery leads to help prioritize product and engineering stories.

Preferred Skills and Experience
• Looking for candidates with some level of understanding or inner knowledge of the preferred skills (below), not necessarily extensive experience. 
o They want to see curiosity and a willingness to learn.
o Critical skills include Snowflake, DBT, and Azure.
• Functional programming skills are also desirable. 
o The manager understands that finding DBT experts in the Twin Cities area may be challenging due to limited usage.
o A 5 out of 10 experience level with DBT is acceptable, and exposure to DBT with the ability to navigate complex models is sufficient.
• The candidate should be able to explain the entire process of using DBT with Snowflake and Fivetran, including CI/CD integration.

Job Description: 
Location: Hybrid (3 days on site) (Cross-Shore Coordination Required)  
Tech Stack: Snowflake, dbt, Fivetran, Azure (ADO, Blob, Functions etc.)  
Senior level data Engineer will lead technical execution and provide architectural guidance across our onshore and offshore engineering teams. In this role, you will be the primary technical point of contact, ensuring that complex data requirements are translated into scalable, resilient, and highly optimized solutions. You will not only build but also influence the standards for a modern data stack centered on Snowflake and Azure, driving "Governance as Code" and operational excellence.  

Key Responsibilities  
1. Technical Leadership & Cross-Shore Guidance  
Engineering Anchor: Act as the primary technical lead for distributed teams, ensuring clarity in requirements and maintaining high standards of execution across time zones.  
Architectural Blueprinting: Engage with Product Owners and Solution Architects to design optimal data product pipelines that serve as the foundational reference for the broader engineering team.  
Resilience Engineering: Design systems for high availability and fault tolerance, ensuring the data platform can recover gracefully from upstream failures.  
2. Data Platform & Pipeline Engineering  
Modern Data Stack Mastery: Engineer and optimize full-lifecycle data pipelines using Fivetran, Snowflake, and dbt, focusing on large-scale, complex datasets.  
Metadata-Driven Automation: Design and implement config-driven or metadata-driven pipelines to increase development velocity and reduce manual overhead.  
Layered Frameworks: Apply advanced modeling techniques (Data Vault, Dimensional/Star Schema) to create high-performance, curated, reusable core datasets and purpose-built datasets optimized for analytics and AI.  
3. Performance & Cost Optimization  
Snowflake Expert: Apply advanced proficiency in Snowflake performance tuning (clustering, warehouse profiling, query optimization) to minimize both latency and platform/tool consumption costs.  
End-to-End Efficiency: Monitor and tune the entire flow from ingestion to transformation to ensure the stack remains performant as data volumes scale.  
4. Governance, Security & DataOps  
Governance as Code: Implement and validate automated data lineage, quality checks, and data classification within the CI/CD workflow.  
Observability & Health: Drive platform reliability by implementing end-to-end observability; proactively monitor data health and enforce rigorous quality gates using dbt.  
Azure Integration: Manage and optimize data flows within the Azure ecosystem, leveraging Azure DevOps (ADO), Blob Storage, and Azure Functions.  

Required Qualifications  
Experience: 8–10 years of experience building and optimizing large-scale, complex data architectures and pipelines.  
Core Stack: Expert-level command of Snowflake, dbt, and Fivetran.  
Cloud Infrastructure: Strong proficiency in Azure services (Storage, Compute, and DevOps/CI/CD).  
Modeling: Proven ability to engineer layered data frameworks using various modeling methodologies such as Kimball, Data Vault 2.0.  
Leadership: Experience guiding offshore teams and conducting technical code reviews to ensure consistency and adherence to patterns.  
Preferred "Good to Have" Skills  
AI/ML Enablement: Experience in building data foundations that enable Machine Learning and Generative AI use cases (e.g., Vector databases, feature stores).  
Advanced Governance: Experience with automated data privacy/masking and advanced metadata cataloging.  
Workday experience is a plus / preferred.