Full Stack Engineer

Job Description:

Serve as a senior full-stack engineer who builds end-to-end data products for the Decisioning practice. You own the work from data pipeline to API to user interface. Data engineering on Databricks and PySpark is the foundation of this role. On top of that, you build full-stack applications and APIs that put data and AI into the hands of media teams and clients. You are an expert in AI-assisted development using Claude Code and Cursor, and you bring a strong eye for QA and data quality to everything you ship.

• Build full-stack applications end to end: data layer, Python APIs, and React/Tailwind front ends that surface data and AI capabilities to users
• Develop and maintain Python APIs (FastAPI or similar) that connect data foundations to client-facing products and agentic systems
• Use Claude Code, Cursor, and other AI coding tools as a daily driver to ship features faster while maintaining quality
• Where AI capability is relevant, build agentic features and tool-use patterns that automate tasks
• 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

• 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

• Hands-on experience with LLM application development: prompt engineering, RAG, function calling, and agent frameworks (LangChain, LangGraph, CrewAI)
• Background in media, advertising, or marketing technology data environments
• Experience with Unity Catalog governance, including attribute-based access control and tag-driven policies
• Experience building MCP servers or integrating MCP into developer workflows
• Open-source contributions or public projects demonstrating full-stack or AI engineering work

Location:

Brand:

Time Type:

Contract Type: