Manager, AI Engineering - Analytics

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.

- 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

- 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

- 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

- 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

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

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

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

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

- Establish the metrics that prove the experience is delivering value

- 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

- 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

- Experience with data modeling and the plumbing that powers analytics

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

- Pragmatic about technology choices and careful about complexity

- Track record of leading projects, mentoring engineers, and driving technical direction

- Direct, kind feedback style and a desire to invest in growing a team

- Clear pull toward leadership, even without prior formal management experience

- 6+ years of software engineering experience, with at least 2 focused on AI/ML or applied AI work (agents, LLMs, evals, or similar)

- At least one agent or LLM-powered system deployed to production that you owned end-to-end

- Hands-on experience building and using evals to measure and improve AI quality

- Solid data engineering or analytics engineering experience, including SQL, modeling, and modern data warehouses

- Track record of shipping production software on small teams and operating across the full stack

- Experience as a tech lead, project lead, or strong mentor, with a desire to grow into formal management

- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience

- Prior experience working on a customer-facing data product, embedded analytics, BI tooling, or a natural language interface over structured data (text-to-SQL, conversational analytics, or similar)

- Experience with semantic modeling layers or modern BI infrastructure

- Experience integrating AI agents with structured data sources

- Background in compliance, security, GRC, or other regulated SaaS verticals

This role will receive a competitive base salary, benefits, and stock, typically in the form of Restricted Stock Units (RSUs). The applicable salary range for this role is: $197,800 - $267,600.