> Markdown version of [/jobs/ext/2735976-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/2735976-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** HUMAN BRAIN BOX LLC - **Location:** New York, NY, United States - **Salary:** $120,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Databases, Customer Data Management, Extract Transform Load (ETL), Web Development, Monitoring of Systems, Python (Programming Language), Open Source Technology, SQL Databases, Tableau (Software), Large Language Models, Snowflake, Backend, Data Analytics, Front End Software Development, Looker Analytics, Data Pipelines - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/analytics-engineer-thesis-company-9908568 ## About the Role * Strong SQL. You've written real queries against real warehouses. Not notebook exercises * Hands-on experience with data pipelines and ETL, including the unglamorous work of keeping them running * Experience with a BI tool such as Metabase, Looker, Tableau, or equivalent * Hands-on experience building with LLMs: prompting, retrieval, context management, tool use, and agentic patterns * Solid fundamentals in databases and APIs * Ability to move from an ambiguous business question to a working prototype to a reliable production system * Strong product instincts and the ability to tell where AI creates real value versus where it's theater * Comfort with ambiguity and a genuine preference for ownership over tickets Nice to Have * Python and modern backend development experience * Front-end or web development experience. Being able to support our website when needed is a bonus, not a requirement * Experience with subscription, DTC, or e-commerce data models * Experience with Snowflake specifically * Experience with vector databases (pgvector, Pinecone, Qdrant, or similar) and RAG architectures * Experience building evaluation or monitoring systems for LLM applications * Experience with AWS, GCP, or Azure * Experience at an early-stage or high-growth company * Contributions to AI research, open-source projects, or technical publications ## Description We're looking for an Analytics Engineer to own our data infrastructure and build the AI systems that change how this company operates. This is a hybrid role by design. Half of it is making sure the data this business runs on is reliable, accessible, and fast: the pipelines feeding our warehouse, the reporting layer every team depends on, and the endless stream of questions from Ops, Marketing, and Growth. The other half is building the agents and automations that let a small team operate like a much larger one. That second half is not a side project or a someday. It's why this role exists in the shape it does, and it's where the person in this seat will grow. You'll work directly with our Director of Data Analytics, who owns the company's data and AI mandate. This is a small team with an enormous surface area, so you'll touch everything from a broken ETL job to an agent architecture nobody has built before. What You'll Do Own the data foundation * Maintain and optimize the ETL pipelines feeding our Snowflake warehouse and Metabase reporting layer * Keep reporting trustworthy. Diagnose anomalies, fix breakages, and improve the things that keep breaking * Build and extend models, views, and dashboards that teams can actually self-serve on Be the internal data resource * Absorb ad hoc requests from Ops, Marketing, and Growth and turn ambiguous business questions into SQL, models, and answers * Work directly with stakeholders to understand what they're really asking, which is rarely what they first ask for * Push the organization toward self-service instead of becoming a query queue Build AI into how the company works * Design and ship AI agents and agentic workflows that reason, use tools, retrieve information, and execute multi-step tasks * Build the skills, orchestration layers, and human-in-the-loop systems that let non-technical teams operate at 10x * Integrate LLMs from Anthropic, OpenAI, and elsewhere into internal tooling and customer-facing products * Build retrieval and semantic search over our ingredient library, research, and customer data * Prototype fast, test with real users, and turn what works into something reliable * Stay close to emerging AI research, models, and tooling, and determine what's actually useful versus hype ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)