Sr. Data Engineer

Come join our Data team!

High velocity, high trust, and high impact with a will to win.

If that resonates deeply with you, this could be your next career move. We're seeking someone who leads with humility, pursues audacious goals, and is motivated by meaningful impact on people and the world.

At FutureFit AI, our core mission is to help more people get to better jobs faster and cheaper, with a specific focus on those facing barriers to opportunity. Our work helps resolve the growing issue of economic inequality, ensuring that no one is left behind in the future of work. Our AI-powered platform brings efficiency and insight to workforce development, replacing outdated systems and unlocking human potential at scale.

Ready to make an impact? Apply today.

Important note: Data shows that men typically apply when meeting 3/10 requirements, while women often wait until it's 10/10. We encourage you to apply if you see a strong (not necessarily perfect) fit.

Your Role

We're seeking a Sr. Data Engineer to join our team.

You will build and own the data foundation of our product: the pipelines, models, and infrastructure that turn raw labor market, skills, and occupation data into the systems that connect people to the right jobs and pathways. This is a hands-on, high-ownership role on a small team. You will design ingestion and transformation pipelines, shape how our data is modeled in the warehouse, make analytics and reporting trustworthy, and build the pipelines that feed our matching and recommendation models in production. You will partner closely with the Engineering, Product, and VP of Data & AI. In this small, nimble team, you will have wide latitude to decide how this platform gets built.

What You'll Own

Pipelines and platform: Design, build, and operate the ingestion and transformation pipelines that bring labor market, customer, and product data into our warehouse as well as into the product — reliably, on schedule, and at growing scale.

Data modeling and quality: Own how our core data is structured, tested, and documented, including the skills, occupation, and career taxonomies at the center of the product. Make data something the whole company can trust without asking first.

Analytics enablement: Build the transformation layer and datasets that power internal analytics, Looker/Quicksight reporting, and the insights we deliver to customers.

ML data infrastructure: Build and maintain the pipelines that feed our matching and recommendation models, and partner with Engineering and Data Scientists to get models deployed, monitored, and improved in production.

Where This Role Can Go

This role starts with the platform, but it doesn't end there. The person who builds our data foundation is the person best positioned to shape what we build on top of it — whether that's moving deeper into modeling and the matching systems your pipelines feed, or into the analytical work that turns our data into insight for customers. We'd rather hire someone with a clear direction they want to grow in than someone who wants to stay in one lane, and we'll build the path with you.

Required Experience

Strong data engineering experience (roughly 4+ years) designing and operating production ETL/ELT pipelines that other people and systems depend on

Fluency in Python and SQL, with real depth in SQL — experience in modeling data in a warehouse/data lake, not simply querying it

Hands-on experience with a modern orchestration and transformation stack (Airflow, dbt, or close equivalents) and with cloud data warehouses

Experience integrating data from varied external sources — third-party data providers, APIs, flat file feeds — including handling schema changes, unreliable delivery, and inconsistent quality from upstream

Comfort working with large, messy, inconsistently structured data, and sound judgment about when to clean it, when to model around it, and when to push back on the source

A builder's instinct for reliability: testing, monitoring, and debugging your own pipelines rather than waiting for someone to report the breakage

Clear communication: you can explain a data model and its tradeoffs to a non-technical audience

Bonus Points

Experience with jobs-and-skills, HR, or labor market data, or with skills/occupation frameworks such as O*NET or ESCO

Experience with hierarchical or taxonomic data — ontologies, classification systems, entity resolution across messy sources

Experience building data infrastructure for ML: feature pipelines, model deployment and monitoring, or tooling like SageMaker

Publications, talks, blog posts, or open source work showing your depth in data engineering

Our Tech Stack for Data

Languages: Python, SQL

Orchestration and transformation: Airflow, dbt

Storage and warehousing: PostgreSQL, Redshift, MongoDB

Cloud: AWS

Visualization and reporting: Looker, Quicksight

Machine learning and NLP: scikit-learn, modern NLP and embedding tooling, AWS SageMaker

Your Education

Your alma mater isn't our focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately, you're our person.

Location

[CA/US Remote] We are open to candidates living anywhere in Canada or the US. For candidates living in Toronto, our office is conveniently located at 325 Front St West (a short walk from Union Station).

Travel Expectations

Although this role is remote, you may be expected to travel up to once per quarter for off-sites and team gatherings.

Compensation

The base salary range for this role is USD $125,000 to $155,000 for candidates based in the United States and CAD $125,000 to $160,000 for candidates based in Canada, regardless of location. As a remote-first company, we benchmark compensation to the national market for comparable roles at institutionally-funded startups, targeting the middle of the market. Bands are designed for the lifecycle of the role — where you enter the band reflects your applied experience and other criteria established by the hiring committee, with room to grow through the band as you grow in the role.

Hiring Journey

At FutureFit AI, our hiring process is designed to help you assess whether this role and our culture are the right fit based on your unique skills, mindset, and experiences. We move fast and work with intensity, so we want you to get a real sense of that from the start.

Each journey includes a mix of interviews and a performance challenge. For this role, that might look like:

Online Application

Initial Screen with Director of People & Culture

Interview with Hiring Manager

Performance Challenge

Final 1:1 Interviews

Final Decision

Generally, this entire process takes around 6 weeks, although the timing can vary due to specific candidate circumstances.

Ready to shape the future of work?

At FutureFit AI, we're not just building a company—we're transforming how talent and opportunity connect. Join our driven team united by a commitment to job seekers and the workforce ecosystems we serve.

Company Snapshot:

Team: 30-50 across US and Canada (hubs in NYC and Toronto)

Customers: Workforce development agencies and intermediaries, government agencies, employers

Industry: SaaS/AI technology

Funding: Bootstrapped 0-1, then raised funding led by JP Morgan

Structure: Growth, Customer Success, Product, Engineering, Data, People & Culture, Finance & Operations

Our Core Principles

Be Curious

Drive to Outcomes

Raise the Bar

Speed Matters

Own It

We Over Me

Use of AI in Hiring

At FutureFit, we use artificial intelligence (AI) tools to make our hiring process more efficient, consistent, and equitable—never to replace human judgment. We use AI in the following ways:

Screening support: AI may help us compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position