Jobs / Canada / Braze INC

Braze INC

Senior Software Engineer II, Decisioning Studio Integrations Team

Toronto Posted today

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ComplianceMachine learningLeadershipData engineeringKafkaPythonSQLGoogle CloudKubernetes

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No sponsorship evidence yet

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No salary bar for this route
Canada has no single salary bar: the employer usually needs a positive LMIA. Pay at or above the provincial median wage + 20% (e.g. C$36.92/h in Ontario) puts it in the high-wage stream. Source: https://www.canada.ca/en/employment-social-development/services/foreign-workers/median-wage.html, rules effective 2026-07-17.

Canada: Express Entry no longer awards ranking points for job offers (since 25 Mar 2025); a job offer still counts toward Federal Skilled Worker eligibility.

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Sponsorship unknown

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About the role

At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can’t wait to meet you. WHAT YOU'LL DO 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. Its results depend on the data it learns from and on the channels that carry its decisions to customers. Our team owns both. We ingest enterprise customer data of any schema and at a very large scale from warehouses, engagement platforms and other enterprise systems. We also deliver the recommendations from our RL pipelines across email, SMS, push, in-app and other channels. These are high-impact, customer-facing features. 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 WHO YOU ARE • 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

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