Manager, AI Engineering - Analytics

About this role

Drata is building the trust layer between great companies - automating compliance, managing risk, and helping organizations prove trust continuously as they scale. We're Dratanauts: a global crew of 600+ professionals united by a culture that rewards integrity, ownership, and raising the bar, no matter where in the world we're working from.

Why Join the Drata Team? At Drata, you're not maintaining legacy compliance software - you're building the agentic AI platform defining what trust looks like for the next generation of companies. Here's what makes the work itself worth showing up for:

- Problems without a playbook: You'll work at the edge of AI and security, building agentic governance, continuous compliance, and real-time trust verification to solve problems that don't have an established answer yet. You're writing it as you go.

- Real ownership, not just process: Our values center on owning outcomes and raising the bar, not checking boxes. You're expected to have opinions and back them.

- A seat at the table: Your perspective is unique and valued. Open debate and diverse viewpoints are built into how decisions actually get made here, at every level.

- Growth at rocketship speed: Drata is scaling fast, which means scope grows fast too. High performers get more ownership, visibility, and experience.

- A crew, not just coworkers: Dratanauts consistently describe a "come as you are" culture with sharp, curious people—the kind of team that makes hard problems genuinely fun to solve. See what they say here https://drata.com/about/careers/life and follow us on LinkedIn https://www.linkedin.com/company/drata/posts/?feedView=all for company news, employee stories, and career updates.

Job Summary:

We are seeking a hands-on engineering leader to head a new, small analytics engineering team at Drata. This team is responsible for the in-product analytics and reporting experience our customers rely on to understand their compliance posture, surface insights from their Drata environment, and turn data into action.

This is a player-coach role. You will be writing code, designing systems, and shipping production AI features alongside a tight group of engineers, while also setting direction, unblocking the team, and growing into the leadership role. It is a great fit for a strong AI engineer who is ready to take their first formal step into management without giving up the keyboard.

The most important thing you bring is a real AI engineering background. You have shipped agents to production, you know what evals are and have built them, and you have strong data fundamentals to back it up.

What you'll do:

Build Alongside the Team

- Stay deeply hands-on by writing code, designing systems, and reviewing PRs

- Own critical paths and pair with engineers on the hardest parts of the product

- Keep close to the codebase and the customer experience even as the team grows

- Set the bar for engineering quality through your own work

Lead a Small Team

- Lead a small, focused team of engineers and grow it thoughtfully over time

- Set clear goals, run good 1:1s, and create an environment where engineers do their best work

- Give direct, useful feedback and help engineers grow in their careers

- Invest in the basics of management: hiring, performance, career growth, and team health

- Partner with leadership to grow into the formal management craft

Own the AI and Data Direction

- Set the technical direction for AI-driven analytics and the data foundation underneath it

- Make pragmatic decisions across the stack, from data modeling to agent design

- Define multi-tenant data access patterns that safely serve customer-scoped data at scale

- Make sound build, buy, and adopt decisions for the team's tooling

- Stay current on developments in applied AI and bring relevant ideas back to the team

Build Natural Language Data Experiences

- Help shape and build features that let users ask questions of their data in natural language

- Ground AI responses in real data, handle ambiguity, and surface uncertainty appropriately

- Keep AI-driven experiences fast, accurate, and trustworthy

- Iterate quickly with design partners to find what works in production

Make Evals a First-Class Practice

- Build the evals, telemetry, and offline/online test loops the team relies on

- Establish eval-driven development as the default workflow

- Define what "good" means for each AI feature and measure it rigorously

- Use eval results to guide model, prompt, and architecture decisions

Ship and Learn

- Drive end-to-end delivery from spec to GA

- Partner with Product on scope, sequencing, and tradeoffs

- Ship iteratively to design partners, instrument adoption, and learn from real usage

- Establish the metrics that prove the experience is delivering value

What you'll bring:

AI Engineering

- Real AI engineering background with at least one agent or LLM-powered system shipped to production end-to-end

- Working knowledge of prompts, tool use, retrieval, and structured outputs

- Understanding of latency, cost, and quality tradeoffs in LLM-based systems

- Familiarity with the failure modes of AI features in the real world

Evals

- Hands-on experience designing and building evals for AI systems

- Comfort with offline benchmarks, regression testing for non-deterministic systems, and online feedback loops

- Ability to articulate how to evaluate an agent before, during, and after launch

- Bias toward measurable quality over vibes

Data Fundamentals

- Strong SQL skills and comfort with modern data warehouses

- Experience with data modeling and the plumbing that powers analytics

- Ability to reason about query performance, data contracts, and multi-tenant access patterns

- Comfort working close to the data, not just on top of it

Hands-On and Pragmatic

- Happy writing code and intend to keep doing it

- Pragmatic about technology choices and careful about comple

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