About the job
At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.
Enterprises spend more than $700 billion on digital advertising, however for the most part, they have very little data & intelligence on how their campaigns are performing. Reconciling that is slow, manual, and mostly wrong, and optimizing those campaigns historically has involved humans and agencies. We are building the data and agent layer underneath that problem, and we are looking for an engineer to help lead that effort.
Responsibilities
Lead the ingest and normalization path for advertising platform data at scale, including rate limits, schema drift, backfills, and restated numbers
Design the aggregation and identity layers that make figures from different platforms comparable
Lead development of agentic workflows that parse performance data, interpret it, present it, and act on it
Set the correctness and evaluation bar in a domain where ground truth is noisy and partially observable
Partner closely with product management, design, and other engineering teams to build intuitive, scalable, and extensible solutions that drive user & business growth
Qualifications
Minimum
10+ years of engineering experience, including time as a tech lead or leading other tech leads, on complex enterprise software projects
Hands-on experience with walled garden advertising APIs (Meta, Google, Amazon, TikTok) or the equivalent from the advertiser, agency, or DSP side
Working knowledge of ad tech data models: campaign hierarchies, attribution windows, conversion APIs, deduplication across sources
Familiarity with measurement approaches such as MTA, MMM, incrementality testing, and privacy-preserving aggregation
Experience shipping LLM-powered systems to production, including evaluation and guardrails
High ownership and bias for action in 0→1 environments: you are comfortable making pragmatic trade-offs, operating with incomplete information, and driving projects from idea through launch and adoption
Strong ability to collaborate across product, engineering, and design teams to align technical strategy with company growth objectives
Preferred
Combination of technical and people leadership, for example, as a TLM
Built or operated measurement infrastructure at a platform, measurement vendor, or large advertiser
Background in experimentation or causal inference