• Identify and prioritize automation opportunities: Partner with stakeholders to understand workflows, gather requirements, and continuously prioritize automation work based on impact (time saved, cost reduced, reliability improved). • Design end-to-end automation: Select the right tools and patterns - serverless jobs, schedulers, cloud-native services, scripts, or AI-assisted workflows - and build solutions from first trigger to final outcome. • Build across the modern data stack: Create and maintain automations spanning AWS, Azure, and Snowflake, integrating with APIs, data pipelines, and internal services. • Engineer production-ready jobs: Write clean, reliable automation code using Python, SQL, C#, and TypeScript/JavaScript while handling scheduling, retries, logging, alerting, and failure modes. • Leverage AI where it makes sense: Build automations both with and without AI integrations - using AI to accelerate classification, enrichment, validation, or decision-making when it delivers clear benefits. • Drive measurable outcomes: Ensure every automation delivers value: track usage, validate impact, tune performance, and iterate or retire workflows based on real results. • Hunt for efficiencies: Proactively spot manual steps, brittle processes, or expensive workflows and replace them with faster, cheaper, more reliable automation. • Raise the bar for everyone: Document patterns, share reusable components, and help teams think more systematically about automation, creating systems instead of one-off scripts.