John Powell

Senior Software Engineer with 11 years of experience building data-intensive platforms, automation workflows, and scalable distributed systems across semiconductor, e-commerce, and IoT domains. Strong background in Data Engineering, backend services, analytics pipelines, and developer productivity tooling, with hands-on delivery across Python, Java, SQL, AWS, and modern ETL/ELT patterns. Experienced in translating complex operational needs into reliable platforms that improve observability, accelerate debugging, and support large-scale business workflows.

Known for partnering across engineering teams to deliver measurable performance, reliability, and productivity gains.

Senior Software Engineer at Arm (2023-01 – Present)

• Built an AI-powered developer productivity platform automating code analysis, debugging workflows, and infrastructure monitoring across distributed semiconductor engineering environments.
• Developed and maintained real-time data ingestion pipelines using Apache Kafka and Spark Streaming to enhance inventory management and personalized marketing analytics.
• Engineered cloud-native data solutions on AWS using Glue, Redshift, and S3, improving scalability and reducing ETL runtime by 35%.
• Implemented dbt for transformation workflows, optimizing SQL models and improving data consistency for downstream analytics teams.
• Automated Apache Airflow DAGs to manage complex batch workflows, improving pipeline reliability and monitoring.
• Collaborated closely with data scientists to provision ML data pipelines, facilitating faster model training cycles.

Full Stack Engineer at Amazon (2018-06 – 2022-12)

• Contributed to customer-facing commerce and internal operational platforms supporting order processing, seller integrations, and personalized shopping experiences across large-scale e-commerce systems.
• Automated data ingestion and processing workflows using Apache Airflow and Python, reducing manual overhead by 40%.
• Designed cloud data architecture on Azure, leveraging Data Factory and Databricks Spark clusters to accelerate data transformations.
• Developed scalable data models to support real-time payment analytics and reporting dashboards, enabling actionable financial insights.
• Supported cross-functional teams in ML pipeline data preparation, aiding improved patient payment behavior modeling.

Full Stack Engineer at Silicon Labs (2015-06 – 2018-05)

• Developed IoT device management and analytics platforms enabling enterprise customers to monitor connected hardware deployments and firmware lifecycle operations efficiently.
• Developed enterprise-grade ETL solutions using Python and SQL on Oracle Cloud Infrastructure (OCI) and Hadoop ecosystem (Spark 2.x).
• Automated data workflows with Apache Airflow, creating reusable DAG templates for ingestion and transformation pipelines.
• Integrated data from various Oracle SaaS applications (ERP, CRM) into unified data lakes to support centralized analytics.
• Collaborated with ML engineers to provision clean, feature-engineered datasets for AI workloads on Oracle Cloud.
• Maintained strong data governance by implementing lineage and quality frameworks using open-source tools.

Bachelor of Science in Computer Software Engineering – The University of Texas at Austin (2011 – 2015)