> Markdown version of [/jobs/ext/1495358-data-operations](https://www.wearedevelopers.com/jobs/ext/1495358-data-operations). 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). --- # Data Operations - **Company:** SIMILE LLC - **Location:** United States - **Salary:** $200,000.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** Information Engineering, DataOps, Alwayson - **Published:** July 30, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ac81b6844f962556 ## About the Role * You are excited about enhancing Simile's data supply chain: You are excited about expanding Simile's global data partnerships and the engine through which we collect data all over the world. * You are interested in building scalable processes: You can hold many live threads at once and still know the status of each. When you leave a project, the person after you can reconstruct every decision and why it was made. * You have intuition for what makes an interesting dataset: You know when a dataset is worth spending time with, when an exclusivity clause is worth paying for, and when to keep looking for alternatives. You are willing to say no to something impressive. * You have negotiating experience or aptitude: You are effective in rooms where you have no leverage and no warm introduction. Some of our highest-value sources will come from convincing an organization to do something it has never done before. * You are comfortable looking at and thinking about data. You think about things like data biases, sample selection, ideal data schema for the simulation task, the tradeoffs of various collection strategies, and what high quality data should be defined as., * Consulting or investing experience: You have experience tackling complex, ambiguous problems in a fast-paced environment. You are a strong first-principles problem solver. * Procurement experience: You have negotiated contracts with vendors and managed competing requests and responsibilities. * Technical fluency: You are comfortable using coding agents to run your own checks and automate your own workflow without waiting on someone else. ## Description Every agent in our simulation is grounded in data from a real person. That makes the supply chain that drives data acquisition and first-party collection the raw material of our product. This is what drives the difference between a model that predicts human behavior and one that approximates it., As a member of Data Operations, you will own the full picture of how data enters and flows through Simile - both the third-party datasets we license and the first-party data we collect. On the sourcing side, you will map the frontier of the data landscape and secure the datasets that make our simulations predictive across new domains and geographies. On the collection side, you will run the supply chain that turns data from real people into grounded agents. This includes designing data collection instruments, interacting with vendors and partners, and the quality and representativeness standards that determine whether a simulation can be trusted. Your core responsibilities will include: * Expanding our coverage of the world: Deciding which populations Simile should be able to simulate next, then going and getting the data that makes it possible. Much of what you want will not be for sale, which means finding who holds it and showing them our vision for the future. * Running Simile's data machine: Expanding and running the operations behind our own human data collection - running the supply chain behind Simile's data engine, which includes panel and field vendor management, incentive structures, throughput, and cost per completed participant. * Finding the richest datasets to improve our simulation of the world: Structuring agreements around how we actually use data - training, fine-tuning, and derivative agent behavior that persists long after a contract term ends. Most data agreements are not written with foundation models in mind, and getting these terms right is the difference between an asset we own and one we license. * Building our always-on feedback loop: Turning what research and forward deployed teams need into a concrete sourcing and supply chain roadmap - and, just as importantly, tracking which data measurably improved the model so the next round of spend is better informed than the last. * Defending data fidelity: Owning the question of whether our agents actually resemble the people they are modeled on. You will set the bar for sample composition and response quality, catch fraud and low-effort participants before they reach a model, and hold the line when a dataset is convenient but not credible. * Trust and compliance: Working with legal so that consent, privacy, and usage rights hold up to the scrutiny of enterprise and government partners. Our access to sensitive populations depends on getting this right the first time. ## Related Videos - [Architecture 3.0: From 90% to 99.999% Reliability in Building AI Systems](https://www.wearedevelopers.com/videos/100190-architecture-3-0-from-90-to-99-999-reliability-in-building-ai-systems) - [Swapping a Data Warehouse at Runtime: Zero-Downtime Migration Without Changing a Single Client](https://www.wearedevelopers.com/videos/100311-swapping-a-data-warehouse-at-runtime-zero-downtime-migration-without-changing-a-single-client) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Opening Keynote: Civic Coding, A Framework for Democratic Tech](https://www.wearedevelopers.com/videos/963-opening-keynote-civic-coding-a-framework-for-democratic-tech) - [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) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [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) - [13 AI Tools You Have to Try](https://www.wearedevelopers.com/magazine/219-13-ai-tools-you-have-to-try)