Analytics Engineer

Location: US Remote

Reports To: Director of Data and Analytics

Salary Range: $140,000-$170,000

Position Summary:

The Analytics Engineer sits at the heart of IEM's modern data stack, turning raw source data into the clean, well-modeled, business-ready datasets that power Tableau dashboards, executive decisions, and self-service analytics across Finance, Production, Supply Chain, and Engineering. Working primarily in dbt and Snowflake, you own the transformation layer between ingestion and the BI surface: staging models, intermediate logic, dimensional models, tests, and documentation. This is a hands-on individual contributor role with real ownership of production data models and a clear path into senior and principal analytics engineering as the team grows.

Key Responsibilities:

Ideal Candidate Profile 

You have 4 to 6 years of experience building production analytics models in cloud environments, with strong dbt and SQL fundamentals and meaningful Snowflake exposure. You think in grain, keys, and tests before you think in dashboards. You write clean, documented, peer-reviewed code and pride yourself on the readability of your YAML. You partner naturally with business stakeholders, translating fuzzy operational questions into well-shaped datasets and surfacing the questions behind the questions. You are comfortable working alongside data engineers on ingestion, with BI developers on consumption, and with finance and operations leaders on definitions. You are excited about AI's role in modern analytics work and already use AI coding assistants and agents as a daily multiplier for SQL, dbt, testing, and documentation.

• dbt Transformation Models: Design, build, test, and document dbt models that turn raw Snowflake data into clean, reliable, analytics-ready datasets across Finance, Production, Supply Chain, and Engineering 

• Dimensional Modeling: Build conformed dimensions, fact tables, and reporting models that balance performance, maintainability, and business user accessibility for Tableau dashboards and ad-hoc analysis 

• Data Quality: Author and maintain dbt tests, monitor freshness, investigate data quality issues end-to-end, and own resolution through to root cause 

• Business Partnership: Partner with cross-functional stakeholders and the Business Intelligence team (Finance, Production, Supply Chain, Engineering) to translate operational needs into scalable data models and reliable metrics. 

• Semantic Consistency: Establish and document standardized metric definitions and reusable data models to ensure consistency, accuracy, and alignment across all reporting. 

• Documentation: Maintain clear model descriptions, column-level documentation, and lineage notes that the team and downstream BI developers actually use 

• Engineering Standards: Participate in code reviews, follow Git workflows and CI/CD practices, and contribute to evolving the team's modeling conventions and deployment standards 

• Source Integration: Partner with the data engineering function on Fivetran and custom ingestion to ensure raw data lands in shapes that downstream models can rely on 

• BI Enablement: Collaborate with BI developers and analysts to structure datasets for optimal Tableau performance and effective self-service analytics. 

• AI-Assisted Development: Use AI coding assistants and agent-based tools to accelerate model development, test generation, refactoring, and documentation. Manage AI agents as part of your daily workflow to increase throughput and quality 

• Continuous Learning: Stay current with the modern data stack and analytics engineering practices, bringing ideas back to the team and helping raise the bar over time 

Qualifications:

• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field (or equivalent experience), with 4–6 years of experience in analytics engineering, data engineering, or BI development, including ownership of production data models  

• Strong SQL skills with experience in data transformation, complex querying, and performance optimization on large datasets  

• Hands-on experience with dbt, including incremental models, tests, macros, snapshots, and documentation  

• Experience working with Snowflake or a comparable cloud data warehouse, along with familiarity with ELT tools (e.g., Fivetran)  

• Solid understanding of dimensional modeling (grain, surrogate keys, slowly changing dimensions, star schemas)  

• Working knowledge of Python for data processing, scripting, or lightweight integrations  

• Familiarity with Tableau or similar BI tools, with an understanding of how data structure impacts performance  

• Experience with Git and modern development practices, including code reviews and CI/CD workflows  

• Strong communication skills, with the ability to translate technical concepts for business stakeholders and gather requirements effectively  

• A collaborative team player who is open to training, mentoring, and working closely with non-technical stakeholders 

• Self-motivated and able to work independently while collaborating across distributed teams  

• Experience leveraging AI coding assistants (e.g., Copilot, Claude) to support analytics engineering tasks such as SQL development, dbt modeling, testing, and documentation 

Preferred Qualifications 

• Experience with manufacturing, construction, or project-based systems (e.g., Procore, ERP platforms like Infor, SAP, Oracle)  

• Familiarity with semantic layers, metrics frameworks, or data cataloging and lineage tools 

Location 

Fully remote within the United States. May require up to 10% travel to IEM facilities for team collaboration, project kickoffs, and stakeholder meetings.