Senior Software Engineer - Data Infrastructure

About Vibe At Vibe.co , we're reimagining how brands reach audiences in the age of streaming. We believe streaming TV is no longer just a brand awareness play, it's the next great performance marketing channel. We're building the infrastructure to unlock this $100B opportunity. Vibe.co provides an Audience First Streaming TV Advertising solution for marketers to unlock TV as a growth channel. Our all-in-one solution combines hyper-targeted audience segmentation, AI-powered insights and recommendations, real-time campaign optimization, and incrementality measurement, giving brands of all sizes the precision and transparency they've come to expect from social and search, but on TV. Trusted by over 10,000 brands, Vibe.co reaches more than 120 million households across 500+ apps and channels, delivering an average 250% return on ad spend and 20% sales lift. The company hit a $100 million revenue run rate in under two years, ranking among the ten fastest software companies to reach that milestone. Founded in 2022, Vibe.co is widely recognized as the category-defining platform in streaming TV advertising, bringing the power of Meta and Google-style performance marketing to the fastest-growing segment in media. Your team You will join the Data team, the backbone that powers every data-driven decision at Vibe — from internal processes to the reporting our advertisers rely on every day. The team operate three complementary stacks: The batch platform, which orchestrates the company's massive and complex datasets that feed Product, Sales, Finance, and ML. Real-time streams used for speedy, lightweight transformations. These feed parts of the stack that need data quickly (spend tracking, retargeting, etc.). The Reporting stack (ClickHouse Cloud + Cube semantic layer), which serves sub-second analytics to advertisers through the Clear platform reporting UI. You will work closely with Product, Data Science, ML, and Engineering teams across all domains (DSP, Performance, Supply, Platform), as well as directly with the customer-facing teams that depend on the reporting layer. Your mission You will design, build, and operate the infrastructure that turns Vibe's massive event volumes (bid, win, impression logs) into trusted, fast, and cost-efficient data products. Your mission is threefold: Build and run the services at the core of the platform — stream processors, aggregation and billing services, the orchestration and reporting layers — with the reliability of systems the business cannot function without. Treat the platform as a product: build the tooling, conventions, and abstractions that let every data, ML or Analytics engineer at Vibe ship faster and more safely. Ensure scalability, performance, and cost efficiency at every layer of the stack, in an environment where data volumes grow as fast as the business. What you will do Platform & Services Ownership Design, build, and operate the production services that move and transform data at scale: real-time event processing on Kafka, batch orchestration on Dagster, and the serving path into ClickHouse Cloud. Own projects end-to-end, from requirements gathering with stakeholders through design, implementation, monitoring, and post-launch iteration. Make architecture choices that scale economically — partitioning, materialization strategy, retention, compute sizing — and quantify the impact of those choices. Developer Enablement & Tooling Build platform-level tooling and conventions — orchestration patterns, testing frameworks, CI/CD, data contracts, self-serve infrastructure — so the team scales faster than its workload. Pave the road: turn recurring one-off solutions into reusable, well-documented building blocks that other teams adopt because they're the easiest path, not because they're mandated. Reliability & Operational Excellence Raise the bar on observability: meaningful alerts, useful dashboards, healthy SLOs — so the team detects and resolves issues before users feel them. Take part in the on-call rotation, lead incident response, and write the runbooks and automation that make pages rare and short. Anticipate scaling pain before it becomes incident-shaped, especially as bid/win log volumes and advertiser usage continue to grow. Performance & Cost Optimization Profile and optimize services, queries, and storage across Kafka, Spark, DuckDB, and ClickHouse workloads, treating compute and storage spend as a first-class engineering concern. Drive cost reviews and reductions for the most expensive systems, and instrument the platform so cost regressions are caught early. Cross-Team Partnership Partner with data, ML, and product engineers to understand their needs, translate ambiguous requests into concrete deliverables, and push back where the right answer is "not yet" or "not this way." Influence upstream producers on schema, semantics, and SLAs so the platform stays simple and reliable. Communicate with senior stakeholders in a clear, synthetic way — surfacing tradeoffs, risks, and decisions rather than implementation detail. WHAT WE'RE LOOKING FOR 5+ years of experience as a software engineer building and operating distributed, data-intensive systems in production. Strong programming skills (Python and/or Rust), with real software engineering discipline: testing, code review, CI/CD, and an instinct for maintainable design. Hands-on production experience with several parts of our stack — stream processing (Kafka), distributed compute (Spark), large-scale storage (Iceberg / object storage), column-store analytics (ClickHouse or similar) — and the ability to pick up the rest quickly. Comfortable owning services from code to production: cloud infrastructure (AWS), Kubernetes, infrastructure-as-code, and the observability to know your systems are healthy. Track record of performance and cost optimization at scale — you can point to specific systems you made measurably faster or cheaper, and explain why. Excellent cross-functional communication: able to adapt your level of detail to the audience, push back constructively, and align stakeholders with conflicting priorities. Pragmatism over purity: bias toward shipping value, with strong judgment about when to invest in abstractions and when to keep things simple. Nice to have: experience in AdTech / programmatic.