Software Engineer III, Insights & Reliability Engineering - Mexico City

<div class="content-intro"><h2 data-pm-slice="1 1 []">About Crunchyroll</h2> <p>Founded by fans, Crunchyroll delivers the art and culture of anime to a passionate community. We super-serve over 100 million anime and manga fans across 200+ countries and territories, and help them connect with the stories and characters they crave. Whether that experience is online or in-person, streaming video, theatrical, games, merchandise, events and more, it’s powered by the anime content we all love.</p> <p>Join our team, and help us shape the future of anime!</p></div><p>Crunchyroll, LLC is an independently operated joint venture between US-based Sony Pictures Entertainment, and Japan's Aniplex, a subsidiary of Sony Music Entertainment (Japan) Inc., both subsidiaries of Tokyo-based Sony Group Corporation.</p> <h2><strong>About the role</strong></h2> <p>We’re expanding the Fan Insights Reliability Engineering team with a Software Engineer III focused on building the next generation of data models, metrics frameworks, and operational insights for our device ecosystem.</p> <p>This role is ideal for an engineer who thrives at the intersection of data engineering, analytics, and product reliability. You’ll shape how we measure device QoE, build scalable data pipelines, and define the ontology and semantic layer that enables actionable insights across the organization. You’ll work closely with our Data Engineering, Platform, and Device teams to turn telemetry into observability tools and real-time metrics that power operational excellence and experimentation.</p> <p>This isn’t a traditional data analyst or dashboard-building role. You’ll own the full journey: from data modeling and metric definition to real-time observability and impact measurement. You’ll help shape how we understand the reliability and performance of our devices and partner ecosystem, driving tangible improvements in Quality of Experience for millions of fans worldwide.</p> <h2><strong>What you will be doing</strong></h2> <ul> <li>Design and build scalable data models and pipelines that measure and monitor the health and QoE of our device ecosystem.</li> <li>Develop and evolve our data ontology and metrics definitions, ensuring consistent and meaningful measurement across devices, partners, and platforms.</li> <li>Partner with Data Engineering to build real-time observability systems that enable proactive detection, alerting, and rapid rollback of problematic releases.</li> <li>Translate complex device telemetry and operational data into clear, actionable insights for engineering and product teams.</li> <li>Collaborate with product, reliability, and engineering teams to instrument new data points and improve data quality across client platforms.</li> <li>Contribute to the development of our A/B testing foundations, enabling data-driven decisions on release quality and user experience.</li> <li>Create and maintain dashboards, metrics catalogs, and data documentation to improve data discoverability and trust.</li> <li>Advocate for data reliability and operational observability, ensuring that pipelines and metrics meet rigorous quality and SLA standards.</li> </ul> <h2><strong>Qualifications</strong></h2> <ul> <li>Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience.</li> <li>5+ years of experience in data-related engineering roles (Analytics Engineer, Data Engineer, Data Modeling Engineer, etc.).</li> <li>Expert in SQL and experienced in data modeling, ETL, and metrics design for operational and product use cases.</li> <li>Strong programming experience in Python (or similar).</li> <li>Deep understanding of data warehouse design, semantic modeling, and ontology frameworks.</li> <li>Proven experience working with data pipelines and workflow orchestration (e.g., Airflow, dbt, Spark, Flink, or similar).</li> <li>Familiarity with data visualization and BI tools (e.g., Tableau, Mixpanel, Mux) and designing ... (truncated, view full listing at source)