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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. The team owns both the data ingestion pipeline and the delivery of recommendations across channels.
In this role, you will lead complex integration projects from design through build and rollout, setting technical direction for the Decisioning Studio integration platform. You'll design and build integrations with cloud data warehouses, customer engagement platforms, and other enterprise systems for both data ingestion and activation orchestration. Key responsibilities include:
• Building ingestion pipelines that handle batch pulls, file drops, APIs, and streaming data while accepting arbitrary schemas and preventing bad data from reaching models
• Designing event-driven, fault-tolerant activation pipelines that deliver recommendations on schedule, recover cleanly from partial failures, and ensure exactly-once delivery semantics
• Designing for multi-region deployment and compliance requirements, including data residency and PII handling
• Owning systems in production, instrumenting them with Datadog, participating in on-call rotation, and driving incidents to root cause
• Collaborating with data scientists, ML engineers, and product managers to translate integration requirements into reusable platform capabilities
• Mentoring engineers and raising the bar for code quality, testing, and design review
This is a high-impact, customer-facing role where you'll work on systems that directly influence how enterprise customers leverage AI-driven personalization at 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 handling partial failures
• Strong grasp of event-driven architecture and reliability patterns: message brokers (Kafka), idempotency, retries with backoff, dead-letter handling, and 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
