Senior Analytics Engineer, Workbench

Fulfillment IQ is a supply chain engineering and transformation company that helps brands, retailers, and 3PLs design, build, and scale high-performance logistics operations.

We work at the intersection of strategy, supply chain, and technology where we solve complex, real-world problems across warehouse design, automation, order management, transportation, and end-to-end supply chain execution.

Workbench is our warehouse design platform, built inside CrossDock Studio, FIQ's product studio. It is early, it is real, and it is backed by Prologis. We are a small team building a genuinely new product, not maintaining a mature system.

You will build the data processing and analytics engine underneath Workbench. This part of the platform is responsible for ingesting customer data that comes in inconsistent shape and format, and turning it into clean, standardized, product-ready datasets on which repeatable analytics workflow can be conducted.

This is a coding role: your deliverable is code, not data. You will write the data ingestion, transformation and imputation logic, build it so it runs automatically against data we have never seen, and produce the metrics and prototypes that our industrial engineers and product team ask for. You will report to the Chief Scientist and work shoulder to shoulder with domain experts and the engineering team.

You do not need to be a supply chain expert on day one. The domain questions come from the people around you. What you need to bring is the ability to write efficient and maintainable code that automates what is currently a manual and time-consuming process, and the judgment to see the repeated pattern and build it into the product instead of doing it by hand a hundred times.

Develop and deploy code that ingests, cleans, reconciles, and standardizes large, heterogeneous customer supply chain datasets, and do it in a way that holds up when the next customer's data looks nothing like the last.

Turn raw data files into the standardized order, inventory, and shipment datasets that Workbench is built on. Find the patterns across customers and build the automation that handles them generically.

Produce the metrics, segmentations, and analyses the industrial engineers and product team need, by writing the code that computes them, not by working a spreadsheet.

Build data visualization demos and prototypes so the team can see what an analysis looks like before engineering builds the production version. You will not own production UI but will give input on its development.

Hand your transformation and analytics code to the engineering team in a state they can take to production and stay the owner of the data logic underneath it.

Lean hard on AI coding tools to move fast and own every line they produce. If you cannot read it, debug it, and defend it, it does not ship.

You have to be able to code, unassisted, when it matters. AI tools are welcome and expected, but the moment the AI is wrong you need to read the error, understand the data, and fix it yourself. This is the single hardest gate in our process, and most candidates do not clear it. We would rather tell you that now.

We work with real, industry data with lots of mismatched SKUs, missing fields, and inconsistent formats. Automating the cleaning and reconciling is a large part of the job, and you have to be honest about what you trust.

Manual analysis is the means, not the end. Our goal is to automate repeated manual work, so it never has to be done by hand again.

You sit between the data and the engineers. You translate what you find into something a dev team can build, and you hold the thread on which capabilities bring users the most value.

Excel and Power BI are not the tools for this seat. If they are your primary way of working with data, you will find this role frustrating and we will not be able to move fast together. That is a real filter, not a preference.

Think chessboard, not ladder. You start by owning the data and analytics engine inside Workbench, and there is real room to grow into data-product ownership and into shaping how the whole platform makes sense of supply chain data. You work directly with the Chief Scientist. Tell us what you want to build and we will help you get there.

Intro conversation. A short call to swap context and make sure the basics line up. Expect a brief, spoken code-thinking question here, we will describe a messy dataset and ask how you would approach it.

Conversation with the Chief Scientist. How you think about data, code, and supply chain, and whether we can make each other better.

Technical. A live working session on a real, messy supply chain dataset. You will load it, clean it, find the patterns that matter, and tell us what you would automate and how you would present it. You will write actual code. You are welcome to use AI tools, on screen, and you will be expected to own what they produce.

Final. Discussion with the Workbench team.

Fluency in Python and tabular analytics at scale, proven on messy, real-world data. You can load an unfamiliar file, inspect it, reshape it, reconcile it, and compute what is asked, and you can do it live. This is non-negotiable and it is what we screen for first.

Experience with tabular data tools like SQL, Polars or Arrow.

The habit of using AI coding tools to go fast while genuinely owning the output. You can read the code, catch when it is wrong, and fix it.

The business sense to see a repeated pattern and turn it into a clear ask for an engineering team, and the communication to present it well.

Enough visualization judgment to say what to show and how, even though you will not build the front end.

A Bachelor's in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field, or equivalent proof you can do the work. We care more about how you code and think than about years.

Bonus points

Supply chain, logistics, warehouse, fulfillment, or order-management data experience.

Experience building analytics capabilities that shipped into a product, rather than internal reporting.

Experience working shoulder to shoulder with a product or engineering team.

Real ownership on a team small enough that your judgment shapes what gets built, working directly with the Chief Scientist on a product that is genuinely new. You will build the capability, not inherit a dashboard and babysit it. We move fast, we automate the busywork, and we back people who take initiative.

FIQ is committed to an inclusive and accessible recruitment process. Consistent with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code, accommodations are available on request at any stage of recruitment and assessment. Contact hr@fulfillmentiq.com and we will work with you to meet your needs.

Your personal information is collected and used for recruitment purposes in accordance with FIQ's Candidate Privacy Notice, available here , and in accordance with applicable Canadian privacy law (PIPEDA). This includes the use of AI-assisted screening described above and cross-border storage or processing that may occur in the United States. By applying, you acknowledge this notice. Questions: hr@fulfillmentiq.com.

Fulfillment IQ is a people-first company built on trust, collaboration, and ownership. We are proud to be an equal opportunity employer and are committed to building a diverse, inclusive, and high-performing workplace. We consider all qualified applicants without regard to any ground protected under the Ontario Human Rights Code, including race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, or disability.

Website: fulfillmentiq.com

LinkedIn: Fulfillment IQ

Spotify: eCom Logistics Podcast Spotify

YouTube: eCom Logistics Podcast YouTube