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United States
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KEY RESPONSIBILITIES
1. Databot Ownership & Expansion
• Maintain, stabilize, and enhance Databot — a Python-based agentic data query tool
• Extend Databot with new data sources and use cases as identified by business teams
• Collaborate with the original Databot developer (peer engineer) for knowledge transfer and architectural decisions
2. Internal Data Tool Development
• Engage directly with internal teams (marketing, finance, product, engineering) to identify unmet data needs
• Translate stakeholder requirements into well-scoped, production-grade internal tools
• Design data pipelines, semantic layers, and consumption interfaces appropriate to each use case
3. Product ionization & Scalability
• Evaluate internal tools for potential external product ionization
Ensure tools meet deployment standards — containerized, Kubernetes-ready, monitored
4. Stakeholder Communication
• Run discovery sessions with internal business owners to understand data access and reporting needs
• Translate non-technical requirements into data product specifications
• Provide regular updates on tool roadmap and delivery status to engineering leadership
TECHNOLOGY ENVIRONMENT
Core Stack (must have):
• Python — primary development language; existing Databot codebase is Python
• Docker — containerization; Databot is deployed via Docker files
• Kubernetes — deployment target for all tools
• Shell scripting — utility scripts in the existing codebase
Data & Analytics (strong preference):
• BigQuery or equivalent cloud data warehouse
• Semantic layer concepts — understanding of how data gets modeled for consumption
• Experience working with dashboards, BI tools, or data reporting pipelines (tool-agnostic; mindset matters more than specific tool)
• Monitoring — instrumentation and observability for data pipelines and agents
AI & Agentic Systems (openness to learn is acceptable):
• MCP
• LLM-based agentic data consumption — experience with data agents or AI-powered query tools
• Google Cloud AI stack: Gemini, Vertex AI
• Familiarity with vector databases a plus (Client’s context)
Non-Negotiable:
• Full-stack engineering capability with Python as the primary language
• Experience working directly with data — curating, wrangling, and building data-driven outputs
• Product mindset: ability to work from ambiguous stakeholder needs to a shipped tool
• Strong communication skills — this role is as much discovery and alignment as it is coding
• US timezone availability for internal collaboration
• Comfort operating as an independent contributor with minimal team structure
• Prior exposure to agentic AI systems or LLM-based data querying
Differentiators:
• Experience shipping internal tools that later became external products
• Background in data lake architecture or data federation across heterogeneous sources
• Startup or scale-up experience where scope and technology evolve rapidly
Bachelor's degree
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