Who We Are Atlan is building the missing context layer for data and AI, helping enterprises close the AI value chasm and finally move AI pilots into production. Today, 95% of AI pilots fail because AI systems don’t understand the context behind data: what it means, how it’s governed, and how it should be used. Atlan unifies this context by connecting to every part of the data and AI stack, enabling teams to build, collaborate on, and activate shared context across data, business, and AI workflows, including for AI agents. Trusted by global enterprises like Mastercard, Workday, General Motors, Unilever, Ralph Lauren, FOX, Nasdaq, and Medtronic , we’re backed by world-class investors including GIC, Insight Partners, Meritech, Peak XV, and Salesforce Ventures The Mission Atlan is on a mission to become one of the most data-driven companies in the world and to create a blueprint for how the best AI-native data teams should operate. We're hiring an AI-native Data Operations Lead to own how Atlan runs its business through data with rigor, speed and empathy. This is a hands-on role at the intersection of analytics, strategy, and AI. You'll own the data foundation that powers Atlan's operating rhythm. Working closely with the C-suite and GTM leaders, you'll ensure strategy is translated into clear metrics, real signals, and decisive action. Why This Role Matters Building the fastest learning and iterating company in the world: AI is fundamentally changing what analytics can be — moving us beyond tidy metrics to learning directly from raw signals like calls, conversations, and documents. At Atlan, we already grade sales calls, analyze support interactions, and mine unstructured data to surface the “why” behind outcomes. This role sits at the forefront of that shift, rebuilding analytics in an AI-native way so the company can learn faster, adapt sooner, and operate with exceptional clarity. Serving as a model for the most forward-thinking data teams: Atlan’s core advantage has always been empathy. We understand our users because we’ve been them. As data teams enter the AI era, even the most sophisticated teams are struggling — optimizing for existing workflows instead of designing for what’s next. We believe the only way to truly help is to live two years ahead of our customers. This role is the learning ground for real, working implementations of AI-native Data Operations. What’s proven internally shapes how we build product, guide customers, and define what “good” looks like for modern data teams. For the person in this role, that means operating at the cutting edge — shaping new practices and influencing how AI and analytics evolve across the ecosystem. What You'll Do Build and own the AI-native data pipeline and operations behind Atlan’s weekly → monthly → quarterly insight cadence, culminating in company-wide, exec and Board reporting. Translate strategic priorities (Big Rocks) into measurable outcomes and clear operational metrics and partner cross‑functionally (Sales, Marketing, CS, Growth) to unify metrics, definitions, and insights. Leverage AI and unstructured data (e.g., call intelligence, notes) to surface faster, deeper insights. Design and maintain dashboards, conversational analytics agents (we’ll learn about this evolving space together!) and data workflows across domains. Drive cross‑functional alignment through data storytelling and executive‑ready communication. What Great Looks Like The company runs on your insights to make faster, better decisions. Data‑driven rhythm established: weekly ops reviews, monthly business reviews, quarterly company-wide reporting. AI‑powered analysis embedded into the GTM engine (e.g., win/loss, pipeline health, forecast accuracy). Consistent, trusted metrics across functions; no surprises at the Board table. What We’re Looking For 7+ years of experience in data analytics, strategy, and/or business operations. Track record of building executive‑level dashboards and reporting in a sales‑led SaaS environment. Strategic thinker who can map qualitative strategy → quantitative insight → business action. Strong communicator — concise, structured, and confident with executive audiences. AI‑native mindset: comfort combining structured (e.g., Salesforce, HubSpot) and unstructured data; bonus for having built or demoed an AI agent or an autonomous workflow. Hands-on expertise in SQL, Snowflake, and modern BI (Sigma or similar). Experience collaborating with cross‑functional leads (GTM, Ops, Finance, CS). (Bonus Points!) Experience in SaaS, AI, or IoT at Series C+ stage companies. Exposure to AI‑based goal tracking or agent‑driven analysis platforms. Why Atlan? Joining Atlan means being part of a global movement to help data teams do their life’s best work. Here’s what you can expect: Competitive Compensation: We benchmark at the top of the market and keep compensation simple: strong base salary, performance‑based variable pay, and impact‑driven equity, so your total rewards grow in step with the value you create over time. Health & Wellness : From Day‑1 health, dental, vision, and mental health to pet‑care perks and flexible health stipends, we design benefits offerings that lead in each country we're in. Flexible Time Off & Leave Policies: We trust you to own your energy: flexible time off and modern leave so you can unplug properly, support yourself and your loved ones, and come back ready to drive an impact. Accelerated Growth & Learning: Develop at an uncommon velocity through cutting-edge tech, complex implementations, and an experienced team that values mastery. AI Native Culture: Atlan is where AI-native builders come to build the systems the future of work will run on. AI isn’t an add-on, it’s woven into how we build, think, and work every day, empowering every Atlanian to move faster and create a bigger impact. Global, Remote-First, High-Trust: Work from anywhere with a diverse team across 15+ countries, in a trust-first, async environment that gives you true flexibility and ownership over how you work. Equal Opportunity Employer Atlan is committed to building an inclusive, diverse, and authentic workplace. We do not discriminate based on race, color, religion, national origin, age, disability, sex, gender identity or expression, sexual orientation, marital status, military or veteran status, or any other legally protected characteristic.

• Collaborate with team members to support translation of business problems into technical solutions • Complete assigned tasks in solution development for data extraction, classification, routing, or search using foundation models • Manipulate and analyze data programmatically following established patterns, and communicate findings clearly • Engineer foundation model prompts under guidance and evaluate using statistical metrics with support • Implement evaluation components using precision, recall, F-1 scores, and accuracy with guidance • Document work clearly with team review • Learn to identify component dependencies and contribute to problem decomposition

• Work with our team of engineers, scientists and human factors professionals to develop machine learning and other algorithm data capabilities for cybersecurity, space, and telecommunications. • This individual contributor role will involve hands-on work collecting data, developing solutions to real-world problems using machine learning. • Developed solutions are actively being productionized to support ongoing cybersecurity initiatives and other crucial infrastructure and security needs. • Collaboratively develop data collection processes designing experiments to ensure quality and usefulness of data. • Develop and evaluate machine learning solutions to interdisciplinary problems in cybersecurity and telecommunications, working with structured (time-series, tabular) and unstructured (text, 3D) data. • Work with human factors specialists, and data/full stack engineers to define system requirements, review developed code, and support translating statistical concepts to coworkers. • Support documentation of analysis results for semi-technical stakeholders. • Occasionally support technical interactions like collaboration, including defining technical requirements for external data collections, and reviewing technical work.

• Design, optimize, and execute complex queries on large-scale medical databases while safeguarding system performance • Collaborate with PhD-level researchers to translate high-level research goals into scalable data pipelines • Process, structure, and prepare DICOM medical imaging data for research-ready AI applications • Balance algorithmic development with production-grade data cleaning and structuring • Own query timing and execution strategies to maintain system stability in production environments • Proactively identify and prevent performance bottlenecks across the data infrastructure