Description

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

Key Skills

Education

Bachelor's degree