Software Engineer (Data & AI)
Polar Analytics: The All-in-One Data Platform for Consumer Brands 🐻❄️
Polar Analytics simplifies data so brands can make smarter, faster decisions without the complexity of traditional tools. Powerful yet user-friendly, it gives DTC brands the insights they need to scale profitably.
🚀 Our mission? To help indie DTC brands compete and grow—faster and more profitably.
What Makes Polar Analytics Unique? 💎
🔥 4,000+ Brands and Growing
We’ve scaled to 4,000+ active merchants (Jan 2025) and are on track for 10,000+ this year.
🤖 Disrupting a Stale Analytics Market
Polar is revolutionizing the Shopify analytics space with modern data infrastructure built for agentic orchestration—positioning us at the forefront of AI-driven commerce automation.
💰 Backed by World-Class Investors
We’ve raised $28.5M with Frst, Point9, and Chalfen Ventures, top-tier B2B SaaS investor known for spotting future unicorns early.
👥 A Team of eCommerce & Data Experts
Our team brings experience from leading eCommerce SaaS platforms & Silicon Valley data unicorns, united by a mission to build the next industry leader.
Who we are
We’re building the data & AI operating system for eCommerce, think Datadog for retail.
We ingest messy Shopify, ads, and retention data, make it reliable, and turn it into decisions teams can actually trust.
We’re hiring engineers who want real ownership.
What you will do
You’ll work on core systems: data pipelines, semantic layers, RAG, AI evaluation, experimentation,and products used daily by thousands of merchants.
This isn’t a ticket-taking role. You’ll:
Own problems end-to-end (design → production)
Tackle hard infra challenges (petabytes, near real-time, high reliability)
Talk directly to users to understand failure modes + edge cases
Ship fast prototypes, then harden them into real systems
Help shape what we build next as we scale
We move quickly, optimize for leverage, and care a lot about doing things right as we grow. We use Claude Code and Cursor very heavily.
If you’re looking for more impact and responsibility than your current role offers, apply below :)
Our Hiring Process 📝
We believe in a structured, fair, and transparent hiring process. While the steps may vary by role, here’s what you can expect:
1. Founder Screen (20 mins): A call to talk through your current/past experience and your motivations and tell you more about Polar Analytics.
2. Technical Interview 1 (1 hour): Here, you'll meet our team to solve a problem live. The interview will focus on real problems we face at Polar.
3. Technical Interview 2 (1 hour): This interview usually consists of a practical case study.
4. Culture Interview (1 hour): A conversation with one of our Culture Champions. We assess your team fit based on our values (see below).
We value your time and effort in the application process, and we aim to provide feedback as quickly as possible.
Two non-negotiable values:
We want to be a 10x startup - starts with everyone having 10x impact
The superceding principle. Prioritize actions with the highest expected impact. If it does not move the needle, do not do it.
Customer obsession - exists only to help users.
We exist to create value for brands and users. Never ship or act in ways that harm them. Every initiative must clearly increase user value or remove friction to realizing value.
Some operating principles
The only way to win is to move fast
Try 100 things vs the competitor’s one attempt
Action precedes information
Every person and every system improves each week.
Growth is compounding.
Hold a weekly retro and a weekly forward plan.
Be ruthless on standards, never rude in delivery.
Disagree directly, commit fully once a decision is made.
We are here to win, not to be right, or to do what we wish
Do what it takes to win. “Not my scope” is not an excuse. Do what makes us win, not what we feel like doing.
Set goals you believe in, then hit them. If at risk, reforecast early with a plan to close gaps.
Behaviors we do not tolerate
Low or no impact.
Lack of desire to win.
Accepting passive failure.
Actions that harm users or sacrifice long‑term user value.
Politics and “winning the argument” over winning the market.