Senior Software Development Engineer, GoDaddy

• 7+ years in data engineering, data platform engineering, or software engineering
• Strong data modeling and data architecture skills, including domain-driven design and building reusable data products for downstream consumers
• 5+ years of professional Python, building APIs, automation, and data or platform tooling, with the ability to troubleshoot distributed data systems
• Building self-service data experiences — APIs, workflows, or tooling that let producers and consumers work independently
• Designing modular, reusable platform patterns — frameworks, plugins, or marketplaces — that scale automation across many teams
• Hands-on experience with AWS (S3, Lambda, Glue, Lake Formation, Redshift, API Gateway), lakehouse architectures and Apache Iceberg in production, and orchestration with Apache Airflo

Desirable

• Help shape how engineers, analysts, and business teams build on and use our enterprise data platform
• You'll design data products and the self-service experiences around them — from the data model to the APIs and workflows producers and consumers rely on — so teams can get what they need without waiting on tickets
• You'll also help build the enterprise data context that lets people and AI find, understand, and trust data
• This role suits a hands-on data engineer with strong data modeling, AWS, and Python skills who enjoys owning a problem from the user's need down to the data underneath
• Design data models and domain-driven building blocks that turn raw data into reusable, well-governed data products for end consumers
• Contribute to the enterprise data context — semantic models, ontologies, and knowledge bases that make data discoverable and usable by people and AI agents
• Build end-to-end self-service experiences for data producers and consumers, including onboarding, SQL automation, data quality monitoring, and the APIs and workflows that expose data products
• Build and evolve AI-assisted and agentic automation that removes manual work, including migration support, impact assessment, and guided workflows for platform and data teams
• Own incident response for the services you build: lead root-cause analysis and after-action reviews, ship durable fixes, and write runbooks so first-line support can resolve recurring issues on their own
• Drive priorities for Platform UX services in partnership with engineering leadership and teams across US and India time zones: turn user needs into a prioritized backlog, weigh trade-offs, unblock users, and own the outcomes of what you deliver