Description
Join us in building the data backbone of Siri’s regression detection system - the platform that enables engineering teams and senior leadership to see, understand, and improve product quality. In this role, you’ll build large-scale data systems that power metrics observability at scale. Success in this role means not only technical excellence, but also the ability to collaborate across teams, communicate effectively and align diverse stakeholders around solving complex, high-impact challenges. You thrive in fast-paced, dynamic environments and are comfortable navigating ambiguity to deliver meaningful, incremental impact. You bring strong problem-solving skills, operate with a high degree of autonomy and have a track record of executing effectively. With a commitment to continuous learning and attention to detail, you actively seek opportunities to innovate and share knowledge. You follow engineering best practices - including unit testing, CI/CD, documentation, monitoring, and alerting - to ensure reliable, maintainable solutions. In this role, you’ll collaborate closely with stakeholders to understand metric needs, provide technical guidance, gather requirements and deliver robust data solutions and intuitive dashboards.
Minimum Qualifications
7 years of development experience and Bachelors or Masters degree in Computer Science or related field or 5 years development experience and PhD in Computer science or related field. Expert knowledge of one or more object-oriented programming languages (Java, Objective-C, C++, Scala, Swift etc) or scripting languages (Python, Ruby, Bash etc.). Experience working with Spark or other distributed data technologies (e.g. Hadoop, Presto, Flink, Druid) for building efficient and large scale data systems. Expertise in development of big data systems and ETL for product metrics and analysis of large data volumes to identify patterns, draw insights and troubleshoot issues. Knowledge of SQL to analyze data, derive insights and drive improvements. Leadership experience, including being a technical lead for complex, cross functional development projects demonstrating good technical judgement and prioritization skills.
Preferred Qualifications
Experience supporting the end-to-end machine learning lifecycle, including data ingestion, feature pipelines, batch inference, and model monitoring in production environments. Hands-on experience building, scheduling, and maintaining data and ML workflows using orchestration frameworks such as Apache Airflow Experience working in cloud environments (AWS, GCP, or Azure) and integrating data pipelines with cloud-native storage and compute services
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