Responsibilities
- Lead projects from initial discovery through design, development, and live deployment with full ownership.
- Create scalable data foundations including data modeling, schema architecture, dimensional modeling, ETL/ELT processes, and handling slowly changing dimensions in production environments.
- Develop full-stack applications using Python and FastAPI for backend services and Next.js for frontend interfaces to enable accessible data and AI experiences.
- Leverage AI-powered coding assistants such as Claude Code to accelerate development while maintaining high standards of quality and technical judgment.
- Implement AI-driven systems like retrieval-augmented generation (RAG), agent-based workflows, and LLM-integrated processing, orchestrated through MCP servers, Skills, and Plugins.
- Manage workflow orchestration using tools including Airflow, Dagster, Prefect, Celery, or Temporal, selecting the most appropriate based on requirements.
- Set up and maintain CI/CD pipelines and cloud infrastructure deployments on AWS and Azure platforms.
- Convert unclear or evolving client needs into well-defined technical architectures and clearly explain design decisions to both technical and non-technical stakeholders.
- Develop and contribute reusable tools, templates, and technical components to enhance the broader data and AI practice.
Benefits
- Fully remote work environment
- In-person team gatherings twice a year to strengthen collaboration and alignment
- High level of autonomy and responsibility across complete project lifecycles addressing real-world business problems
- Culture that prioritizes actual use of AI tools over superficial adoption
- Exposure and access to a comprehensive suite of modern data and AI technologies
- Opportunities for career growth in data architecture, platform strategy, or deep AI engineering
Work Arrangement
Fully remote
Team
High-autonomy, high-ownership environment solving end-to-end client challenges with modern AI and data technologies
Other
- Fully remote.
- Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face.
- High-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.
- A team that takes AI tooling seriously and expects you to use it, not just name-drop it.
- Access to the full modern data and AI stack — no one-tool shops.
- Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.
