Mission
This role exists to build and maintain the data infrastructure that powers dynamic, catalogue-driven advertising at scale. The CDA team owns the feeds and data ingestion layer — the systems that transform large-scale product catalogues and transaction data from retailers into the building blocks of high-performing ads across Facebook, Instagram, Snapchat, Pinterest, and beyond. Without this engineer, the platform doesn't scale, the data doesn't flow, and advertisers can't deliver.
This is a hybrid role requiring onsite presence 4 days per week.
Outcomes — What Success Looks Like in 6–12 Months
- Owns features end-to-end: Has shipped multiple features independently — from technical design through deployment and monitoring — with minimal oversight and strong production quality.
- Meaningfully improves data ingestion reliability: Identifies and resolves bottlenecks or failure points in the feeds/ingestion pipeline, measurably improving uptime, latency, or data accuracy for downstream teams.
- Earns trust as a platform thinker: Demonstrates clear API boundary design and system decisions that balance the needs of consuming teams with long-term platform health — recognized by peers and leads.
- Elevates team code quality: Consistently delivers thorough code reviews that catch real issues, improve maintainability, and raise the bar on testing and observability practices across the team.
- Grows as an AI-native engineer: Has integrated AI-assisted development tools meaningfully into their workflow and can articulate specific ways those tools have expanded the scope or quality of what they deliver.
Skills — Core Technical Capabilities
Required
- 4+ years building and maintaining production backend services or platforms
- Proficiency in at least one modern backend language (Node.js, Go, Python, Ruby, or equivalent)
- Experience designing services that process high-volume data — ingestion, transformation, or analysis at scale
- Ability to define clean API boundaries and reason clearly about system design tradeoffs
- Solid practices around testing, monitoring, and incident response as first-class engineering concerns
- Strong communicator who collaborates effectively with engineers, PMs, and designers across a multidisciplinary team
Preferred
- Exposure to distributed systems, high-availability platforms, or large-scale data pipelines
- Experience with cloud or containerized environments (AWS, Docker, Kubernetes)
Competencies — Behaviors We Like to See
Ownership without prompting
- Takes features from design to production and follows through on monitoring, iteration, and cleanup — without waiting to be asked
- When something breaks or degrades, moves toward the problem rather than waiting for assignment
Platform thinking over short-term fixes
- Makes system design decisions that account for how other teams will consume and build on the work
- Actively flags technical debt and advocates for the right tradeoff between speed and long-term health
AI-native craft
- Has a specific, credible point of view on how AI tools have changed their engineering practice — not just faster, but differently scoped
- Brings intellectual curiosity to tooling and is willing to experiment, evaluate, and share what works
Collaborative standard-setting
- Uses code review as a genuine craft conversation — gives feedback that makes the code and the engineer better
- Invests in pairing and knowledge sharing with junior teammates without being asked
