Principal Analytics Engineer

Protolabs•Maple Plain, MN

3d•$131,500 - $197,300•Hybrid

About The Position

This is a fixed hybrid role on-site Tuesday, Wednesday, Thursday at HQ in Maple Plain, MN. We are looking for a Principal Analytics Engineer to join our team. This role is responsible for building the trusted data foundations that power business decision-making across the company. Working at the intersection of business, analytics, and technology, you will translate complex data and business processes into scalable, well-governed data products. As a senior technical leader, you will partner closely with stakeholders across the business, Analytics, Data Engineering, Architecture, AI, and Software Engineering teams. A key focus will be leading the migration from legacy DOMO reporting to our modern Snowflake and dbt platform, ensuring business logic and reporting knowledge are transformed into sustainable, future-ready data models. The ideal candidate combines deep SQL and analytics engineering expertise with a strong understanding of business processes, metrics, and systems. You thrive in ambiguity, bridge the gap between technical and business teams, and help ensure data accurately reflects how the organisation operates.

Requirements

10+ years of experience in Analytics Engineering, Business Intelligence, Data Modelling, Data Warehousing, or related disciplines
Significant experience partnering directly with business stakeholders to understand processes, metrics, operational workflows, and business challenges
Significant experience designing and maintaining analytical data models across multiple functional domains
Expert-level SQL skills with the ability to simplify, optimise, document, and explain complex business logic and transformations
Deep understanding of analytical data modelling and data product development
Strong business acumen and curiosity, with the ability to quickly understand new domains and connect business processes to data structures
Strong experience working in ambiguous environments and bringing structure to complex data landscapes
Exceptional stakeholder management and communication skills, with the ability to engage effectively with both technical and non-technical audiences
Ability to work comfortably across multiple domains while maintaining an end-to-end understanding of how business processes connect across the organization
Strong understanding of data quality, validation, testing, reconciliation, and root-cause analysis practices
Experience establishing standards, best practices, and governance approaches across analytical environments
Strong documentation and knowledge sharing practices
Experience mentoring and influencing peers and teams without relying on direct authority
Experience with Git-based development workflows and CI/CD practices

Nice To Haves

Experience with dbt or equivalent modern SQL-based transformation frameworks
Experience leading or contributing to migrations from legacy analytics environments into modern data platforms
Experience with DOMO, including understanding of legacy reporting, ETL, and business logic implementations
Experience working with Finance, Human Resources, or other highly governed and business-critical data domains
Experience supporting enterprise reporting, KPI governance, semantic modelling, or data governance initiative
Experience with BI platforms such as Power BI, Tableau, Looker, or similar technologies
Exposure to data cataloguing, metadata management, lineage, observability, or governance platforms

Responsibilities

Lead the design, ownership, and continuous improvement of enterprise analytics data models across multiple business domains
Drive the migration of legacy DOMO transformation logic to our modern Snowflake and dbt data platform
Partner closely with business stakeholders to understand processes, metrics, operational workflows, controls, and decision-making needs
Work with the Analytics team to ensure business definitions, metrics, and data models remain trusted, consistent, and aligned across the organisation
Collaborate with software development teams and source system owners to understand how data is generated, structured, and maintained
Translate complex and often ambiguous business requirements into scalable, maintainable, and well-documented data products
Serve as a senior subject matter expert in analytics engineering, SQL, data modelling, metric definition, and business data interpretation
Establish and promote best practices for data modelling, documentation, testing, and analytics engineering across the data platform
Partner with the Principal Data Engineer and Data Architect to align data architecture, ingestion strategies, transformation frameworks, and consumption patterns
Collaborate with the Analytics Quality Engineer to continuously improve data quality, testing, governance, validation, and reliability standards
Work closely with AI teams to ensure analytical datasets and semantic models support AI, machine learning, and advanced analytics use cases
Serve as a cross-functional subject matter expert, quickly developing expertise across new business domains while helping preserve and scale organisational knowledge through data products and documentation
Mentor and support Analytics Engineers, fostering excellence in SQL, data modelling, stakeholder management, business analysis, and analytics engineering best practices
Lead complex investigations across business processes, source systems, data pipelines, metrics, and reporting to identify root causes and drive resolution
Champion trust in data by ensuring critical datasets and models are accurate, transparent, well-governed, and aligned with real-world business operation
Create and maintain clear, comprehensive documentation covering business processes, metric definitions, transformations, data lineage, assumptions, and operational logic