Senior Software Engineer, Data and Product Systems

About CityScout

CityScout turns fragmented government records into real estate development intelligence. We connect zoning, permitting, planning, parcel, ownership, geospatial, company, and contact data across dozens of U.S. markets so customers can identify development opportunities earlier and act with better information.

Our platform is a long-running system. Information moves from municipal websites and public documents through acquisition, parsing, normalization, storage, geospatial analysis, entity resolution, AI classification, APIs, and customer-facing applications. Reliability and data quality depend on how well those layers work together. Our team is remote-first and relies on clear ownership, documented decisions, and intentional collaboration.

The role

Senior engineers at CityScout are expected to understand the complete system.

You do not need to arrive knowing every service. You will be expected to develop a working understanding of how data moves from the original public source to the customer and to reason across that process when making decisions or investigating failures.

This is a hands-on role. You will design systems, write and review code, diagnose production problems, guide other engineers, and improve the technical and operational foundations that let a small team support a large data footprint.

What you will do

• Develop a system-level understanding of CityScout's ingestion, processing, enrichment, storage, API, and product layers
• Own major technical initiatives from architecture through production operation
• Trace data and production failures across services to their root cause
• Build and improve customer-facing applications, pipelines, scrapers, and internal platforms
• Set standards for reliability, testing, observability, maintainability, and data quality
• Review engineering work for correctness and fit with the broader system
• Identify recurring problems and replace local patches with durable improvements
• Use coding agents extensively while maintaining clear technical accountability
• Improve the prompts, context, tools, and review systems through which the team works with AI
• Help early-career engineers become independently productive without becoming a bottleneck
• Translate customer and company priorities into executable technical decisions
• Communicate architectural decisions, risks, and tradeoffs to technical and nontechnical stakeholders

What strong performance looks like

• You can explain how information moves from source records to the customer experience.
• You identify the actual layer responsible for a problem instead of treating symptoms locally.
• You ship directly and increase the speed and quality of the engineers around you.
• You simplify the system and reduce repeated operational work.
• You make sound decisions with appropriate consideration for customer impact, security, data integrity, and maintainability.
• You create clarity across ambiguous, cross-functional work.
• You understand the code and architecture produced with your direction, regardless of how much was generated by an agent.

What we are looking for

• A record of owning production systems with meaningful complexity
• Strong architectural judgment grounded in hands-on implementation
• Ability to understand unfamiliar systems and reason across service boundaries
• Deep fluency with coding agents and AI-assisted engineering workflows
• Strong debugging, systems thinking, and data reasoning
• Experience setting technical standards and reviewing consequential work
• Ability to coach engineers and improve team output
• Clear written and verbal communication
• High ownership and sound judgment under ambiguity

Helpful experience

• TypeScript, Next.js, Node.js, and serverless systems
• AWS infrastructure and production operations
• PostgreSQL, PostGIS, or large geospatial datasets
• Web scraping and heterogeneous data ingestion
• Document parsing, entity resolution, or data enrichment
• LLM-driven classification and extraction systems
• Agent infrastructure, MCP servers, or tool-enabled AI workflows
• Real estate, GIS, public records, or government data

Our stack

• TypeScript and Next.js
• Node.js and AWS Lambda
• PostgreSQL and PostGIS
• Drizzle
• Playwright-based scrapers
• AI classification and enrichment pipelines built on current Claude models
• AWS infrastructure across the platform

How we evaluate candidates

The practical assessment resembles senior work at CityScout: an unfamiliar system with a realistic defect, extension, or architectural constraint, with normal use of coding agents and documentation. We evaluate:

• Ability to form an accurate model of the system
• Quality and completeness of the implemented result
• Recognition of data, operational, and architectural risks
• Use and supervision of AI tools
• Understanding of submitted code
• Quality of technical communication
• Ability to distinguish a local fix from a durable system improvement