Analytics Engineer (Data Modeling Focus)

Job Title
Analytics Engineer (Data Modeling Focus)

Role Overview
We are looking for a data modeling focused professional to turn raw operational data into structured, reliable datasets used across the business.
This role sits between engineering and operations. You will work with order, shipment, and financial data to build clean models that support reporting, analysis, and decision making.
This is not a pipeline-only or dashboard-only role. The focus is building trusted data.

Responsibilities
Build Data Models
- Transform raw and semi-structured data into clean datasets
- Define table grain such as order, shipment, and item level
- Create reusable models that reduce repeated logic
Solve Complex Data Relationships
- Connect datasets that do not naturally align such as carrier invoices and order data
- Handle incomplete or inconsistent data using structured logic
- Create matching and reconciliation approaches
Define Business Logic
- Translate business concepts like revenue, shipping cost, and margin into consistent definitions
- Align with stakeholders on how metrics should be calculated
- Ensure logic lives in the data layer, not in reports
Improve Data Usability
- Reduce reliance on raw queries
- Build datasets that are reliable and easy to use
- Document logic and assumptions clearly
Collaborate Across Teams
- Work with Operations, Finance, and IT
- Identify upstream data issues and communicate impact
- Contribute to modeling standards and best practices

Requirements Core
- Strong SQL skills
- Experience working with messy or imperfect data
- Strong understanding of data modeling concepts
- Fact vs dimension
- Table grain
- Handling duplicates and edge cases
- Ability to structure ambiguous problems into clear solutions

Nice to Have
- Experience with data pipelines or ETL workflows
- Experience with BI tools
- Familiarity with tools like dbt or modern data stacks
- Experience with eCommerce, logistics, or financial data

What We Are Looking For
- Strong problem solver who thinks in systems and structure
- Comfortable working with messy real world data
- Able to communicate with both technical and business teams
- Balances accuracy with practicality