Analytics Engineer - Remote

About the role

WHO WE ARE:

BaubleBar designs accessories that make life more fun. Founded in 2010, the Company is known for its statement-making styles that create joy and bring people together. Today, BaubleBar’s unabashedly optimistic perspective can be found across multiple accessory categories that are sold direct to consumer and at 8,500+ retail stores globally.

WHAT WE'RE LOOKING FOR:

We are seeking an Analytics Engineer to join our Data Analytics team and help build and maintain the data foundation that supports reporting and decision-making across the organization.

This is a hands-on, early-to-mid-career role for someone with an excellent foundation in SQL, data modeling, and modern analytics engineering practices. The Analytics Engineer will work closely with analytics leadership to translate business requirements into reliable, scalable data models while developing deeper technical and business expertise.

Our current data stack includes Fivetran, dbt, GitHub, Snowflake, and Power BI. Experience with our exact technology stack is not required. Strong technical fundamentals, sound data-modeling instincts, and the ability to learn quickly are more important than experience with a particular platform.

WHAT WE WANT YOU TO DO:

Data Modeling & Analytics Engineering

- Write clean, efficient, and maintainable SQL to transform raw business data into reliable, analytics-ready datasets

- Develop and maintain staging, intermediate, dimensional, fact, and reporting-layer data models

- Translate business requirements and established architecture into production data models

- Apply dimensional modeling concepts with a strong understanding of grain, relationships, and dependencies

- Refactor and improve existing SQL and data models as business requirements evolve

- Enhance existing data models for self-service and AI-powered analytics by improving metadata, descriptions, and semantic context for tools such as Snowflake Cortex AI

- Build and maintain data-quality tests and documentation

Development & Version Control

- Use Git and GitHub to manage analytics code through branches, commits, pull requests, code reviews, and deployment workflows

- Develop and test changes before deploying them to production

- Evaluate upstream and downstream dependencies before modifying existing models

- Follow established SQL, dbt, documentation, and version-control standards

Data Quality & Troubleshooting

- Investigate data-quality issues and discrepancies between reporting and source systems

- Trace data through source, transformation, and reporting layers to identify root causes

- Troubleshoot failed transformations and scheduled analytics jobs

- Validate changes and develop sustainable solutions rather than relying on manual fixes or workarounds