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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # TELECOMMUTE Manager of Data Engineering - Python / Google Cloud Platform - **Company:** The Search Solutions, LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Microsoft Word, Artificial Intelligence, Airflow, Amazon Web Services, BigQuery, Cloud Computing, Data as a Services, Data Architecture, Information Engineering, Data Files, Data Infrastructure, Data Stores, Data Systems, Django Web Framework, Graph Database, Python (Programming Language), Neo4j, Systems Development Life Cycle, Cloudera, SQL Databases, SQLAlchemy, Google Cloud, Reliability of Systems, Fastapi, Virtual Agents, Restful APIs, Data Pipelines, Microservices - **Published:** July 30, 2026 - **Apply:** https://www.dice.com/job-detail/4a2db0ff-becb-4ffb-af75-b5793a60a0da ## About the Role 10+ years of Software/Data Engineering experience, with a proven history of leading technical teams. Proven track record of designing and building production-grade AI data systems. Technical Stack: Advanced Python (FastAPI, Pydantic, SQLAlchemy) and SQL mastery for building scalable microservices. AI: Hands-on experience with Vector Databases (Pinecone, ChromaDB), RAG pipelines, and GraphRAG patterns. Data Tooling: Deep experience with Prefect (preferred) or Apache Airflow or Cloud Composer, BigQuery, and DataProc. Cloud Infrastructure: Experienced in working with cloud platforms (Google Cloud Platform, AWS) and deploying data workloads and pipelines at scale. Coding Agent: Demonstrated proficiency in using coding agents to accelerate the SDLC and plan and code complex engineering tasks. ## Description The Manager of Cloud and Data is a hands-on role and will be the primary architect and technical lead for the data infrastructure powering our next-generation Agentic AI products. Acting as a hands-on leader, you are responsible for the team's overall delivery, translating complex product requirements into actionable technical tasks for a small engineering squad. You will design the multi-modal data stores (Vector and Graph) that serve as the "Active Memory" for autonomous agents while remaining deeply embedded in the codebase to drive execution. Key Responsibilities Technical Execution: Lead the technical delivery by translating high-level product roadmaps into actionable development cycles. You will own the task breakdown and manage the workflow to ensure high-quality output from the team Strategic Data Architecture: Architect and directly implement multi-modal data pipelines that process structured parts catalogs and unstructured sources (PDFs, Word, PNG/SVG diagrams) into specialized Vector and Graph data stores. Knowledge Layer Development: Design and optimize the storage of embeddings in Vector Databases (e.g., Pinecone, ChromaDB, Vertex AI Search) and Graph Databases (e.g., FalkorDB, Neo4j) to enable multi-step agentic reasoning across disparate data sources. Agent-Driven Development: Deeply integrate autonomous coding agents into your daily workflow to plan, generate, and refactor data infrastructure and microservices. Evaluation Pipelines: Collaborate with AI Engineers to build "Gold Dataset" pipelines used for the automated verification, retrieval quality measurement, and "confidence scoring" of AI outputs. Microservices: Develop and maintain scalable data services and RESTful APIs using Python (FastAPI/Django) to provide structured, validated data. Cloud Operations: Deploy and monitor high-scale data workloads on Google Cloud Platform (Vertex AI, BigQuery), ensuring system reliability, security, and cost-effectiveness ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) ## Related Articles - [Got AI ideas but no money? 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