Manager, AI Engineering - Analytics

Drata•San Francisco, CA

10d•$197,800 - $267,600•Hybrid

About The Position

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.

Requirements

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
Bias toward measurable quality over vibes
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
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
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
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

Nice To Haves

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
Prior tech lead or team lead experience
Previous experience at high-growth SaaS companies

Responsibilities

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, 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
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
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

Benefits

Stock equity
Up to 100% employer-paid premiums for medical, dental, and vision coverage for employees and their dependents
Comprehensive wellness benefits and healthcare concierge services
401(k) plan
Company-paid life and disability insurance
Tax-advantaged spending accounts
Discounted voluntary offerings
Paid Parental Leave policy, after six months of employment
Kindbody fertility and family-building benefits
Dedicated leave specialists
Generous annual stipends for both professional and personal development
Access to a wide range of internal learning opportunities
Flexible vacation policy
Paid holidays