Loopreturns

USA, Canada

Posted on: 26 June 2024

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Analytics Engineer

Loop is in an unparalleled position to provide data to our merchants that no one else is able to replicate. Data will be at the core of everything we do and some of our most critical work as we grow. Our future is to create data-based products that use modern techniques for delivering not only data but invaluable insights. We’ll identify problems our merchants don’t even know they have and use our data to create solutions that thrill merchants with their ability to predict and forecast their customer’s needs.

As an Analytics Engineer at Loop, you’ll significantly impact our ability to solve merchant problems and fulfill merchant needs. Our cloud-based data warehouse fuels the great work our Analytics and ML teams do. Your job is to model the raw data into clean, well-defined, analytics-ready datasets, acting as the bridge between Data Engineering, Analytics, and ML and championing scale across our data architecture.

We’ve listed what we think you’ll be spending your time on. We’re growing fast, and growth means the challenges we’ll work on together will change as we lead Loop through new and different phases.

This is an indispensable role with us, so we’ll be looking for you to have examples of when you’ve tackled these challenges throughout your career. We’ve laid out the experience we think is important to set you up for success in this role. But, we appreciate that different humans will solve problems in different ways, so we don’t expect you to fit exactly in a box of requirements.

At Loop, we believe that flexibility and choice are what allow you to do your best work. With our Blended Working Environment, you have options ranging from joining our HQ office (in Columbus, Ohio), opting into a Hub (a location with 4+ team members), or staying totally secluded (our version of remote). Our team is spread across the United States, select provinces in Canada (Ontario & British Columbia), and the United Kingdom. Wherever you live, you can create the work environment that best matches your preferences and lifestyle.

Technologies we’re excited by: dbt, SQL, Snowflake, Spectacles, Git, Looker, Hex, Fivetran

What You’ll Do:

Design, develop, and extend production-quality dbt code with an eye toward performance, scale, and maintainability.
Through a DataOps lens, collect business requirements, define successful analytics outcomes, and produce scalable database designs, data models, and reusable data assets.
Support our data analysts, data scientists, ML engineers, and other team members by building, reviewing, and approving data model changes that help us continuously deliver value back to the business.
Collaborate with the Analytics and Data Engineering teams on data pipelines, ETL, data analysis and visualizations, etc.
Develop and maintain documentation that increases the understanding and utilization of our data assets.
Support the implementation of our analytics, semantic, and metrics layers.
Support the execution of medium to large-scale data projects that have a significant impact on multiple stakeholders across Loop.

Your Experience:

3+ years of hands-on experience as an analytics engineer, data analyst, data engineer, or equivalent.
1+ year of hands-on experience working with dbt.
Significant experience in data modeling and data warehouse architecture, with solid SQL experience.
Business acumen and experience with one or more functional areas such as Product, GTM, Finance, Marketing, Customer Experience.
Familiarity with software development best practices applied to data and analytics (DataOps), primarily related to version control, CI/CD, automation, and testing.
Self-motivated and self-managing, with a solution-oriented mindset.
Exceptional verbal, written and listening skills and the ability to bring consensus and communicate with a diverse audience.

What we value:

Provide value back to the business.
Progress over perfection.
Collaboration.
Growth mindset.
Diverse and inclusive thinking.
Accountability and high-quality work.
Make new mistakes.

Nice to have:

Familiarity with dimensional modeling/data warehousing concepts.
Python experience.
Experience building and maintaining semantic layers and self-service content in Looker for non-technical end-users.
Experience with project management.
SaaS, e-commerce, and startups.

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