Manager, AI Engineering - Analytics

Why Join The Drata Team?

The best way to understand the Driver’s Mindset is to see it in action. We’re an award-winning, mission-driven team of 600+ people worldwide, united by a culture that values trust, speed, and continuous growth.

See the Speed: Watch our CEO, Adam Markowitz, discuss the hyper-growth journey, from $0 to $100M ARR in just four years

Hear the Voice of the Team: Explore our "Life at Drata" page for employee testimonials on our collaborative and the growth opportunities available.

Experience the Impact: See why we are consistently recognized on Fortune's Best Workplaces lists.

Connect with Us on Socials: LinkedIn - follow us for company updates, employee stories, and career news.

What You'll Do

• Build Alongside the Team
• 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
• Ship and Learn
• Drive end-to-end delivery from spec to GAP
• 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
• 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 complexity
• Bias toward shipping and learning over over-engineering
• Comfortable working across the full stack on a small team
• Ready to Lead
• Track record of leading projects, mentoring engineers, and driving technical direction
• Strong written and verbal communication
• Direct, kind feedback style and a desire to invest in growing a team
• Clear pull toward leadership, even without prior formal management experience

Requirements

• 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
• Strong written and verbal communication
• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience

Bonus Qualifications

• 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
• Background in compliance, security, GRC, or other regulated SaaS verticals
• Previous experience at high-growth SaaS companies