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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Python AI Data Engineer - **Company:** Openmind Technologies - **Location:** United States (Remote available) - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, Artificial Intelligence, BigQuery, Data Infrastructure, Data Transformation, Data Security, Python (Programming Language), Marketing Information Systems, Shell Script, Google Cloud, Large Language Models, Multi-Agent Systems, Data Layers, Containerization, Data Lakes, Kubernetes, Data Analytics, Data Pipelines, Docker, Database Tools and Utilities, Programming Languages - **Published:** June 30, 2026 - **Apply:** https://www.dice.com/job-detail/46bb8f79-8954-4051-b198-fcbbd2e46859 ## About the Role * 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), * 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 ## Description Client is building an internal AI data platform and is seeking a Lead AI Data Engineer to own and expand its flagship internal tool - Databot, an AI-powered data agent that enables business teams across marketing, finance, and operations to query and interact with company data through natural language. This is a high-visibility, high-autonomy role. The engineer will be deeply embedded with internal stakeholders across the organization and must combine strong technical execution with a product mindset. The scope extends beyond maintenance: the successful candidate will build new internal data tools and, over time, contribute to productionizing these capabilities for external use. Given the volume of internal collaboration required - all key stakeholders are US-based - this role requires US timezone availability. Night-shift coverage from India may be considered for exceptional candidates., 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. Productionization & Scalability * Evaluate internal tools for potential external productionization * 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, The platform operates on internal business metadata - cluster data, invoice data, marketing data, and external market data. No customer PII is involved. Data volume is in the triple-digit gigabyte range (~100-999 GB). 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