Hire someone who's already lived inside the problem.
Nurses, paralegals, foresters, civil engineers — the people here learned to code after a real career somewhere else. That background doesn't disappear. It shows up in better questions, faster ramp-up, and software that actually fits the work.
30 engineers here, each with real years in their industry before the code.
Sarah Jenkins
Full Stack Engineer
The Past
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2012 - 2020Civil EngineerManaged $50M infra projects
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ExpertiseAutoCAD Logistics
The Now
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2021 - PresentPython DevBuilding PropTech SaaS
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StackDjango React
In her words
"Eight years on construction sites taught me which edge cases actually break things on a Tuesday morning."
Less translation. More building.
When the person writing the code already worked in the industry, a lot of the back-and-forth just goes away.
They notice the right things
A missing audit trail, a workflow that doesn't match the regulation, a form that misses an edge case — they catch it because they've lived it.
Shorter ramp-up
Days, not months, before they're asking the useful questions in standups.
Fewer "what does that mean?" calls
They already know what HIPAA, IFRS, or a Phase II inspection actually involves — because they used to work under it.
Engineers who started somewhere else
Chloe Russell
Risk analyst who switched from flagging model drift to building the models
"My risk models kept getting too complex for spreadsheets. I took an ML course to learn better tools and realized I wanted to build models full time."
Caleb Foster
Bench scientist writing the analysis code she used to wait on IT to build
"I was already writing R scripts to analyze my sequencing results. Switching to Python and building proper tools was a natural next step once I realized how much time our lab wasted waiting on IT."
Jordan Blake
Claims adjuster applying risk-scoring instincts to ML models
"Claims adjusting is really about spotting patterns in incomplete information. ML engineering is the same problem with better tools."
Nina Shah
Supply chain manager building the visibility dashboards he always needed
"I built forecasting dashboards in every BI tool on the market before deciding it was faster to just write the code myself. That side quest became my career."
Camila Flores
Sustainability officer quantifying carbon with code instead of spreadsheets
"Corporate sustainability reporting was all manual data gathering. I learned Python to automate it, then realized I wanted to build the platforms that make carbon accounting standard."
Ruby Wallace
Clinical researcher who pipelines her own trial data now
"Running clinical trials is project management with regulatory constraints and messy data. Data engineering gave me the tools to handle the messy-data part properly."
People who changed careers, and built something with it
A few stories of how a previous career keeps showing up in the work.
Chloe Russell
Risk analyst who switched from flagging model drift to building the models
"My risk models kept getting too complex for spreadsheets. I took an ML course to learn better tools and realized I wanted to build models full time."
Caleb Foster
Bench scientist writing the analysis code she used to wait on IT to build
"I was already writing R scripts to analyze my sequencing results. Switching to Python and building proper tools was a natural next step once I realized how much time our lab wasted waiting on IT."
Jordan Blake
Claims adjuster applying risk-scoring instincts to ML models
"Claims adjusting is really about spotting patterns in incomplete information. ML engineering is the same problem with better tools."
Nina Shah
Supply chain manager building the visibility dashboards he always needed
"I built forecasting dashboards in every BI tool on the market before deciding it was faster to just write the code myself. That side quest became my career."
Camila Flores
Sustainability officer quantifying carbon with code instead of spreadsheets
"Corporate sustainability reporting was all manual data gathering. I learned Python to automate it, then realized I wanted to build the platforms that make carbon accounting standard."
Ruby Wallace
Clinical researcher who pipelines her own trial data now
"Running clinical trials is project management with regulatory constraints and messy data. Data engineering gave me the tools to handle the messy-data part properly."
Two ways in
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