Data / Web Developer (On Site - Boca Raton, FL)
Position Overview
We're looking for a well-rounded Data & Web Developer to join our small data team in Boca Raton, FL. The role blends three things: tracking down and scraping data that isn't handed to you in clean form, cleaning and normalizing it into something reliable, and building the web apps, tools, and Power BI dashboards that put it in front of the people who need it. We're not looking for a deep specialist or a senior architect — we want a capable, versatile developer who's comfortable owning a problem from the raw, messy source all the way through to a working tool or dashboard.
As a commercial real estate owner/operator, much of our most valuable work involves spatial and points-of-interest (POI) data, parcel and property records, and public-records research. You'll own problems end to end: finding and scraping the data, cleaning and normalizing it into dependable datasets, and then building the web interfaces and Power BI reports that let our leasing, acquisitions, and asset-management teams actually use it.
Solid, practical engineering is the foundation — across data, the web front-end, and reporting. What makes the role interesting is the range: some weeks you're scraping a stubborn county portal and normalizing what comes back, others you're shipping a dashboard or a small internal web app.
Key Responsibilities
Finding and sourcing the data
- Independently scope ambiguous business questions and determine what data could answer them — then locate, acquire, and evaluate that data, including obscure or unconventional public sources that aren't packaged as ready-to-use datasets.
- Source data from public records, government and GIS portals, county property-appraiser and parcel data, POI and spatial sources, web-scraped material, APIs, and aerial or street-view imagery — constructing proxy signals when a direct measurement doesn't exist.
- Scrutinize unfamiliar data before trusting it. Test its coverage, freshness, and gaps, confirm it against other sources, and be honest about how far it can be trusted.
Cleaning, normalizing, and pipelining the data
- Build and maintain the data pipelines that turn messy, inconsistent source data into clean, reliable, repeatable datasets — handling ingestion, parsing, deduplication, and transformation.
- Normalize and reconcile data across sources — standardizing formats, matching and de-duplicating records, and keeping things consistent as new data comes in. Set up storage and retrieval that stays manageable as volume grows.
- Build custom, fit-for-purpose tools for novel problems — scrapers, parsers, matchers, and pipelines — starting with a simple working version, then hardening what proves useful into something dependable and maintainable.
Building web apps and tools
- Build internal web apps and tools that let non-technical teams search, filter, and explore the data — designing simple, usable interfaces, not just the plumbing behind them.
- Develop and maintain both the front-end and the back-end of these tools, wiring them up to the data, APIs, and pipelines behind them.
- Build lightweight APIs and integrations that connect our data to the tools, dashboards, and services that consume it.
Putting AI to work where it helps
- Use LLMs as practical tools where they save time — prompting, structured outputs, and simple retrieval/RAG workflows that help with parsing, extraction, and other real data tasks.
- Where it's useful, lean on vision or multimodal models to pull information out of imagery, site plans, and scanned documents.
Reporting, analysis, and delivery
- Build and maintain Power BI dashboards and reports that turn the data you've assembled into something leasing, acquisitions, and asset management can act on day to day.
- Dig into the data you assemble to answer the underlying business question — surfacing the patterns, outliers, and signal that matter to leasing, acquisitions, or asset management.
- Verify that a solution actually answers the question before anyone relies on it — checking outputs against ground truth and catching the quiet failures, not just the obvious ones.
- Translate technical results into something the business can use, and collaborate with colleagues across the company to define problems and deliver solutions.
Required Qualifications
The core — non-negotiable
- Demonstrated resourcefulness in sourcing data. Real experience finding, scraping, or assembling data that wasn't handed to you in clean form — ideally public records, government or GIS portals, APIs, and other unconventional sources — and turning it into something useful.
- Comfort operating in ambiguity. Able to take an underspecified problem, choose a sensible first approach, and adapt as the problem's real shape comes into focus.
Engineering foundation
- Solid Python skills. Comfortable writing clean, maintainable Python for working with data, APIs, and messy, unstructured sources.
- Practical data skills. Building pipelines, cleaning and normalizing messy real-world data, matching records across sources, and writing solid SQL to store, query, and shape it.
- Web development. Able to build a web app end to end — a modern JavaScript/TypeScript front-end (e.g. React) and a back-end or API to support it (Python or Node). You don't need to be a design specialist, but you can put together something clean and usable.
- Power BI. Hands-on experience building reports and dashboards in Power BI — data modeling, DAX, and connecting to live data sources — that non-technical people can actually use.
- Practical delivery tooling. Comfort with version control and a working understanding of how to deploy, monitor, and maintain what you build. Familiarity with containerization and cloud platforms is useful; deep specialization is not required.
Comfort with AI tools
- Some hands-on experience with LLMs as working tools. Prompting, structured outputs, and simple retrieval/RAG workflows. You'll use these regularly, but deep ML or data-science expertise isn't expected.
Analytical grounding — you can grow this with us
- Working grounding in applied analysis and core ML/statistics. Some exposure, for example regression, classification, or time series. This is a plus, not a requirement — if you're strong at sourcing, normalizing, and building, you can grow the analytical side with us.
General
- Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field — or equivalent practical experience. We care more about what you can build than your credentials.
- Ability to work primarily on-site in Boca Raton, FL, and to thrive in a fast-paced, collaborative environment managing multiple projects at once.
- Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders and translate business needs into practical solutions.
Preferred Skills & Experience
- Experience with geospatial or POI data and tools (GIS portals, ArcGIS/REST services, parcel data, geocoding), or a demonstrated ability to ramp into unfamiliar data domains like these quickly.
- Exposure to commercial real estate, property, or public-records data — or genuine curiosity about it. No prior CRE experience is required; the willingness to dive into county records, deeds, leases, and permits matters more.
- Experience building data infrastructure on a cloud platform (Azure preferred; AWS or GCP also valuable), and with containerization and CI/CD where it supports reliable delivery.
- Exposure to using vision or multimodal models in workflows that combine text and images.
- Familiarity with techniques for finding patterns or anomalies in large, messy datasets.
- Experience designing clean, intuitive interfaces or data visualizations — making technical results easy for non-technical people to grasp. Familiarity with mapping or charting libraries (Leaflet, Mapbox, D3, or similar) is a plus.
- Background in applied economics, finance, or related quantitative domains, especially for forecasting, risk modeling, or decision support.
What We Offer
- Opportunity to build new data tools, web apps, and dashboards from the ground up that directly impact business operations and client outcomes.
- Close collaboration with a small, highly technical team where your contributions will be visible and meaningful.
- A culture that values resourcefulness, rigor, and continuous learning.