Description
We're looking for a Senior Software Engineer II to join the Decisioning Studio Integrations team building BrazeAI Decisioning Studio™. Decisioning Studio is a reinforcement learning platform that runs continuous experiments to personalize customer engagement for each individual.
In this role, you will:
- Lead complex integration projects from design through build and rollout, and set technical direction for the Decisioning Studio integration platform
- Design and build integrations with cloud data warehouses, customer engagement platforms and other enterprise systems, for both data ingestion and activation orchestration
- Build ingestion pipelines that handle every way customers send data, including batch pulls, file drops, APIs and streaming, and accept arbitrary schemas without letting bad data reach our models
- Build event-driven, fault-tolerant activation pipelines that deliver recommendations on schedule, recover cleanly from partial failures and never send twice
- Design for multi-region deployment and compliance requirements, including data residency and PII handling
- Own your systems in production. Instrument them with Datadog, take part in the on-call rotation, and take incidents all the way to the root cause
- Work with data scientists, ML engineers and product managers to turn integration requirements into reusable platform capabilities
- Mentor engineers and raise the bar for code quality, testing and design review
Requirements:
- 6+ years of backend engineering experience building and operating distributed systems in production
- A track record of technical leadership: owning complex projects end to end, making architecture decisions and setting direction for other engineers
- Experience building data pipelines or integrations that move very large volumes of data between systems, including schema changes, backfills and partial failures
- A strong grasp of event-driven architecture and reliability patterns: message brokers such as Kafka, idempotency, retries with backoff, dead-letter handling, and at-least-once versus exactly-once tradeoffs
- Proficiency in Python and SQL
- Hands-on experience with cloud infrastructure (GCP preferred) and Kubernetes
- Experience with AI coding assistants, and the judgment to know when they help and when they don't
- A clear communicator who works well with engineering, data science and product
- A self-starter who finds problems and works around obstacles without waiting for direction