• Owns medium-sized projects and features for the platform; begins influencing teams that consume the platform. • Lead the design and implementation of new platform features (e.g., adding support for a new model in AWS Bedrock). • Independently build and manage the platform's integrations with tools. • Optimize the platform for cost, performance, and scalability. • Mentor junior engineers on platform development best practices. • Serve as the primary administrator and subject matter expert for our low-code/no-code automation platforms (Zapier, n8n, Tines). • Partner directly with Marketing, Sales, Operations, and Analytics teams to understand their challenges and identify opportunities for AI-powered automation. • Implement and manage robust CI/CD pipelines for deploying AI integrations and automation workflows using tools like Terraform and native cloud services.

• Design and deploy scalable, resilient multi-cloud architectures (Azure, AWS, GCP) • Create, maintain, and refactor complex Terraform modules from scratch • Develop and manage Ansible playbooks, roles, and collections • Enable the business to leverage AI by managing and scaling cognitive services • Translate high-level business requirements into technical blueprints • Build and maintain robust delivery pipelines in Azure DevOps

• Build and operate scalable backend services and internal APIs for the AI platform. • Integrate LLMs and AI tool execution into reliable, production-ready workflows. • Own production reliability for AI platform infrastructure through observability, alerting, and incident response. • Improve performance and cost efficiency of AI runtimes through profiling, tuning, and capacity planning. • Contribute to shared infra: CI/CD, observability, deployments

• Develop reusable ingestion frameworks (Python/Airflow/AWS Glue) for APIs and unstructured sources beyond Fivetran, handling various data formats (JSON, Parquet, etc.). • Own the end-to-end Medallion (bronze/silver/gold) architecture for core domains, ensuring robust lineage and metadata across diverse data sources. • Implement data observability (native tests, alerts, lineage hooks); lead incident management and root-cause analysis (RCA) for data. • Help standardize reusable “paved-road” patterns (e.g. CI templates, ingestion operators) to improve developer productivity. • Prepare datasets for AI/LLM use cases (feature stores, embeddings/RAG prep).