Backend Software Engineer — Data Platform & AI Data Products
About The Position
You’ll join the Data Platform team, responsible for building the backend services and “data products” that power how data moves through the company. We create the core platform primitives - high-quality event streams, reliable access layers, and developer-friendly APIs/tools - so teams across the org can self-serve what they need and ship faster. You’ll design, build, and run backend services that create value from our company data, and make our data platform more self-serve (DIY)- so product and engineering teams can easily create and operate event-driven architectures, publish/consume streams, define access models, and ship data products end-to-end. You’ll also build LLM-adjacent services such as prompt categorization/taxonomy, enrichment, and metadata systems that turn raw telemetry into trusted, usable products.
Requirements
- 6+ years building production backend systems (distributed systems, APIs, data services).
- Strong skills in at least one backend language (e.g., Go, Python, Java, Rust) and API design (REST/gRPC).
- Demonstrated ability to own services end-to-end: design docs, implementation, testing, deployment, on-call, and iteration based on real usage.
- Strong engineering fundamentals: clean, maintainable code, thoughtful abstractions, strong design patterns, and systems that are easy to evolve.
- Solid data modeling and query skills (SQL) and experience with at least one of: Streaming/eventing (Kafka/PubSub/Kinesis, etc.) Workflow/compute (Airflow/Spark/Flink/Trino, etc.) OLTP/OLAP stores and data lakes (Postgres + warehouse/lake tech)
- AI augmentation mindset: You understand the state-of-the-art ways engineers use AI/LLMs to build software faster and better (e.g., coding copilots, agentic workflows, eval-driven iteration, retrieval/knowledge grounding), and you apply this thoughtfully to your own development and to the systems you build.
- You have engineering judgment and you understand when AI Augmentation can fail or create team dynamics issues, putting mitigation mechanisms and guardrails in place.
Nice To Haves
- Experience building self-serve platforms (control planes, templates/golden paths, paved roads, multi-tenant services).
- LLM/AI product exposure: prompt/response telemetry, eval datasets, embeddings/RAG metadata, model/tool traces, privacy-safe logging.
- Security and governance experience: least-privilege access, auditability, data retention, PII handling.
Responsibilities
- Identify, design, and develop foundational data infrastructure components capable of handling millions or billions of events daily
- Analyze and improve the robustness and scalability of existing data processing infrastructure
- Partner with product teams to understand functional requirements and deliver solutions that meet business needs
- Write clear, well-tested, and maintainable infra-as-code and software for both new and existing systems
- Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance
- Participate in an on-call rotation to address critical incidents when necessary
Benefits
- We offer competitive compensation, startup equity, health insurance and other competitive benefits.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
