Staff Data Engineer - Governance

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<h3><strong>About the Role</strong></h3> <p>At Blip, data is more than a byproduct, it's a strategic asset. Our Data Platform powers everything from internal decision-making to the next generation of AI-driven experiences. We're now looking for a <strong>Sr. Staff Data/Platform Engineer</strong> to play a foundational role in scaling and evolving this platform.</p> <p>This role is ideal for someone who is passionate about solving deep engineering challenges, working across large-scale distributed systems, and translating real-time data into meaningful intelligence across the company.</p> <h3><strong><br>Your Mission</strong></h3> <p>You’ll be the technical backbone of our Data and Platform Engineering team, owning critical platform decisions, mentoring engineers, and designing future-proof, low-latency, high-throughput systems that power analytics, machine learning, and real-time business logic.</p> <p>As a senior individual contributor, you’ll work side-by-side with product, infrastructure, and AI teams, helping shape Blip’s data foundation for years to come.</p> <h3><strong><br>Key Responsibilities</strong></h3> <ul> <li><strong>Platform Engineering & Distributed Systems</strong><strong><br></strong> Design, implement, and optimize distributed data systems using technologies like <strong>Kafka, Flink, Spark, and Delta Lake</strong>, ensuring scalability, fault tolerance, and high performance across streaming and batch workloads.</li> <li><strong>Streaming & Real-time Data Pipelines</strong><strong><br></strong> Build and maintain low-latency, high-throughput pipelines capable of handling billions of events per day, powering real-time dashboards, ML feature stores, and intelligent products.</li> <li><strong>Data Architecture & Systems Design</strong><strong><br></strong> Translate business requirements into robust, scalable architectural patterns, integrating best practices across <strong>data modeling, orchestration, data governance, and observability</strong>.</li> <li><strong>Engineering Excellence & Code Quality</strong><strong><br></strong> Write high-quality, maintainable code and establish patterns that raise the engineering bar. Collaborate in code reviews and mentor other engineers in software craftsmanship.</li> <li><strong>AI Infrastructure & Decision Systems</strong><strong><br></strong> Help bridge traditional machine learning workloads with modern AI-based decision systems. Support the evolution of our ML pipelines into agent-based or model-context protocol (MCP)-enabled workflows.</li> <li><strong>Collaboration & Leadership</strong><strong><br></strong> Work cross-functionally with Product, AI, and Software Engineering teams to drive platform adoption and unlock new capabilities. Act as a technical leader and sounding board on system evolution and architecture choices.<br><br></li> </ul> <h3><strong>What We're Looking For</strong></h3> <p><strong>Experience</strong></p> <ul> <li>8+ years in Data Engineering, Platform Engineering, or Software Engineering roles.</li> <li>Demonstrated expertise building and scaling <strong>distributed systems and data platforms</strong>.</li> <li>Deep hands-on experience with <strong>Apache Kafka</strong>, <strong>Apache Flink</strong>, <strong>Apache Spark</strong>, <strong>Delta Lake</strong>, or similar technologies.</li> <li>Strong knowledge of <strong>streaming architectures</strong>, real-time data processing, and event-driven systems.</li> </ul> <p><strong>Technical Foundations</strong></p> <ul> <li>Solid foundation in <strong>software engineering</strong>: design patterns, testing, CI/CD, versioning, clean code.</li> <li>Proficiency in modern languages such as <strong>Scala, Java, or Python</strong>.</li> <li>Expertise in <strong>SQL</strong>, <strong>data modeling</strong>, and performance tuning for analytical and transactional workloads.</li> </ul> <p><strong>Bonus Points</strong><strong><br></strong></p> <ul> <li>Experience implementing or working with <strong>Model Context Protocol (MCP)</strong> or modern AI infrastructure paradigms.</li> <li>Familiarity with <strong>ML Ops</strong>, feature stores, and model deployment frameworks.</li> <li>Knowledge of <strong>data governance</strong> principles, observability stacks, and RBAC/RLS at scale.</li> </ul> <p> </p> <article class="text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&:has([data-writing-block])>*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" data-turn-id="86c09730-cbf8-4f4e-9fce-716955ee5afb" data-testid="conversation-turn-2" data-scroll-anchor="true" data-turn="assistant"> <div class="text-base my-auto mx-auto pb-10 [--thread-content-margin:--spacing(4)] thread-sm:[--thread-content-margin:--spacing(6)] thread-lg:[--thread-content-margin:--spacing(16)]…