Senior Software Engineer - Data Infrastructure

About the company

Streaming TV is no longer just a brand awareness channel. It's becoming a core part of the performance marketing mix, alongside search and social. the  is building the platform that makes TV work like the rest of the acquisition stack: targeted, measurable, and optimized for results.

the  is an Audience First Streaming TV Advertising platform that gives marketers the tools to reach the right audiences, optimize campaigns in real time, and understand what's actually driving business outcomes. With hyper-targeted segmentation, AI-powered recommendations, real-time optimization, and incrementality measurement, the company brings the precision and accountability of digital advertising to the biggest screen in the house.

Today, more than 12,000 brands use the  to reach 120 million+ households. And in August 2026, the  became a Walmart company, opening up a new chapter for the business and the industry. With Walmart's first-party data and purchase signals now part of the the company platform, advertisers can connect TV advertising to real-world transactions and measure the impact of CTV with a level of precision previously associated with platforms like Google and Meta.

This is a unique moment to join the company. With Walmart's scale, data, and reach behind us, we're building the next generation of TV advertising and creating a more measurable, performance-driven future for the channel.

You'll join the Data team, the backbone behind every data-driven decision at the company, from internal operations to the reporting advertisers check every day. The team runs three interlocking stacks: a batch platform that feeds Product, Sales, Finance, and ML; a real-time streaming layer that powers spend tracking and retargeting; and a reporting stack built on Click House Cloud and Cube that serves sub-second analytics through the company's Clear platform.

This role exists because bid, win, and impression volumes are growing as fast as the business, and the platform needs an owner who can turn that scale into reliable, cost-efficient infrastructure rather than a growing pile of incidents. You'll get end-to-end ownership of systems the business can't run without, direct influence over the tools every data, ML, and analytics engineer at the company uses daily, and a mandate to treat performance and cost as core engineering problems, not afterthoughts.

• Design and operate real-time event processing on Kafka at production scale

• Build and run batch orchestration pipelines on Dagster

• Own the serving path into Click House Cloud from ingestion through query

• Drive projects end-to-end from stakeholder requirements through post-launch iteration

• Set architecture decisions on partitioning, materialization, retention, and compute sizing

• Build orchestration patterns and testing frameworks the whole team relies on

• Establish CI/CD and data contracts that reduce manual review overhead

• Turn recurring one-off fixes into reusable, documented building blocks

• Design self-serve infrastructure that teams adopt without being told to

• Build alerting and dashboards that catch issues before users feel them

• Set SLOs that reflect real business risk, not vanity metrics

• Lead incident response during on-call rotations

• Write runbooks and automation that shorten and reduce pages

• Anticipate scaling limits before bid/win log growth turns them into incidents

• Profile and optimize services across Kafka, Spark, DuckDB, and Click House

• Treat compute and storage spend as a first-class engineering metric

• Lead cost reviews on the platform's most expensive systems

• Instrument the platform to catch cost regressions early

• Translate ambiguous requests from data, ML, and product engineers into concrete deliverables

• Push back on requests that aren't ready or aren't the right approach

• Influence upstream teams on schema, semantics, and SLAs

• Brief senior stakeholders on tradeoffs and decisions, not implementation detail

5+ years building and operating distributed, data-intensive systems in production

• Strong programming skills in Python and/or Rust, with real testing and code review discipline

• Production experience with several of:
  Kafka, Spark, Iceberg or object storage, Click House or similar column-store analytics

• Experience owning services from code to production, including AWS, Kubernetes, and…