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Salary: USD 156,364 - 279,957 / annual
Braze is seeking a Senior Software Engineer II to join the Decisioning Studio Integrations team, which builds BrazeAI Decisioning Studio™—a reinforcement learning platform that runs continuous experiments to personalize customer engagement at scale. The team owns both data ingestion from enterprise systems and delivery of RL-driven recommendations across channels (email, SMS, push, in-app, etc.).
In this role, you will lead complex integration projects from design through build and rollout, setting technical direction for the Decisioning Studio integration platform. Key responsibilities include:
• 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 batch pulls, file drops, APIs, and streaming data while accepting arbitrary schemas and preventing bad data from reaching models
• Build event-driven, fault-tolerant activation pipelines that deliver recommendations on schedule, recover cleanly from partial failures, and guarantee no duplicate sends
• Design for multi-region deployment and compliance requirements, including data residency and PII handling
• Own systems in production: instrument with Datadog, participate in on-call rotation, and drive incidents to root cause
• Collaborate with data scientists, ML engineers, and product managers to translate integration requirements into reusable platform capabilities
• Mentor engineers and raise the bar for code quality, testing, and design review
This is a high-impact, customer-facing role working on systems that process very large volumes of data at enterprise scale.
REQUIREMENTS:
• 6+ years of backend engineering experience building and operating distributed systems in production
• 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 failure handling
• Strong grasp of event-driven architecture and reliability patterns: message brokers (Kafka), idempotency, retries with backoff, dead-letter handling, at-least-once vs. exactly-once tradeoffs
• Proficiency in Python and SQL
• Hands-on experience with cloud infrastructure (GCP preferred) and Kubernetes
• Experience with AI coding assistants and judgment about when they help vs. when they don't
• Clear communicator who works well with engineering, data science, and product teams
• Self-starter who finds problems and works around obstacles without waiting for direction
BONUS:
• Experience integrating with cloud data warehouses (BigQuery, Snowflake, Redshift)
• Experience integrating with customer engagement platforms (Braze, Adobe, Salesforce Marketing Cloud, Klaviyo)
• Experience building multi-region systems under data residency and compliance requirements
• Experience running ML systems in production
