Senior full-stack engineer role building end-to-end data products for Decisioning practice. Responsibilities include building full-stack applications with data pipelines, Python APIs (FastAPI or similar), and React/Tailwind front ends. Use AI-assisted development tools (Claude Code, Cursor) daily. Develop agentic features automating tasks. Ensure QA and data quality with tests, validation frameworks, and observability. Mentor mid-level engineers and collaborate on architecture and standards. Required: 8-10 years software engineering experience, deep Python expertise, full-stack development, production API design, AI-assisted coding tools, QA and data quality engineering, cloud infrastructure (Azure preferred), and strong communication. Preferred: LLM application development, media/advertising/marketing tech experience, Unity Catalog governance, MCP server experience, open-source contributions.
What you'll do
Build full-stack applications end to end: data layer, Python APIs, and React/Tailwind front ends that surface data and AI capabilities to users
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Develop and maintain Python APIs (FastAPI or similar) that connect data foundations to client-facing products and agentic systems
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Set the QA and data quality bar for the engineering team: write tests, build data validation frameworks, and instrument observability
Mentor mid-level engineers through code reviews, pairing, and technical guidance on full-stack practices
Collaborate with onshore leads across the engineering and craft practitioners on architecture decisions, standards, and business requirements
Contribute to reusable component libraries, Claude Code & Cursor skills, and shared platform services that scale across clients
Partner with product designers and BI to turn static reporting into interactive, AI-powered experiences
Requirements
8 - 10 years of professional software engineering experience with deep Python expertise
Demonstrated ability to build full-stack applications end to end, including React + Tailwind CSS front ends
Strong experience designing and building production APIs in Python (FastAPI, Flask, or similar)
Expert-level proficiency with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) including agentic coding patterns, context engineering, and shipping production code with these tools daily
Solid grasp of QA practices and data quality engineering: unit and integration testing, data validation, and observability
Experience with cloud infrastructure (Azure preferred) and modern deployment patterns (containers, CI/CD)
Strong written and verbal communication for collaboration across distributed onshore and offshore teams
Preferred: Hands-on experience with LLM application development: prompt engineering, RAG, function calling, and agent frameworks (LangChain, LangGraph, CrewAI)
Preferred: Background in media, advertising, or marketing technology data environments
Preferred: Experience with Unity Catalog governance, including attribute-based access control and tag-driven policies
Preferred: Experience building MCP servers or integrating MCP into developer workflows
Preferred: Open-source contributions or public projects demonstrating full-stack or AI engineering work
