• Own the technical architecture of the data platform end-to-end: ingestion, storage, transformation, orchestration, serving, and observability layers. • Author and maintain the platform architectural vision document; lead quarterly architecture reviews to assess alignment with organizational goals and technology trends. • Define and evolve the target-state architecture for the platform, establishing a multi-year technology roadmap in partnership with engineering leadership. • Evaluate emerging technologies, frameworks, and make evidence-based adoption recommendations. • Serve as the final technical escalation point for complex design questions, cross-team conflicts, and build-vs-buy decisions affecting the platform. • Lead the planning, execution, and delivery of multi-quarter platform initiatives involving multiple engineers, cross-functional dependencies, and significant organizational impact. • Break down large, ambiguous programs into scoped workstreams; assign technical leads per workstream, define milestones, and manage cross-team dependencies and risk. • Drive initiative kick-offs with clear problem framing, success criteria, architectural constraints, and delivery phasing — from 0-to-1 exploration through production hardening. • Maintain stakeholder alignment throughout delivery: proactively communicate status, surface trade-offs, and escalate blockers to leadership before they become risks. • Lead post-mortems and retrospectives for large initiatives; document and socialize lessons learned to raise the organizational bar on delivery excellence. • Exercise functional oversight across the EDAP team: review technical designs, set quality gates, approve architectural decisions, and ensure consistency of implementation patterns. • Define, document, and socialize platform engineering standards including coding conventions, testing requirements, CI/CD practices, schema design guidelines, and SLA frameworks. • Establish and own the platform's technical review process (design review): triage incoming projects, chair review sessions, and ensure decisions are documented and traceable. • Identify and drive resolution of technical debt, redundancy, and architectural drift that impede platform reliability, developer productivity, or scalability. • Partner with security, compliance, and infrastructure teams to ensure platform systems meet governance, data privacy, and regulatory requirements by design. • Serve as a primary technical mentor for junior and senior data platform engineers across the platform; provide structured coaching on system design, technical communication, and engineering judgment. • Conduct and lead design reviews, architecture critiques, and technical deep-dives that strengthen the overall capability of the engineering team. • Define what technical excellence looks like at each level of engineers and participate in calibration discussions. • Represent the data platform in engineering-wide forums, all-hands, and external venues (conferences, open-source communities, recruiting events). • Build a culture of documentation, reliability, and platform-as-a-product thinking across all data platform engineering functions. • Participate in goal planning cycles as a technical voice; define engineering-led goals that improve platform reliability, developer experience, and data quality.

• You build out the cloud and infrastructure for a production-grade platform • You introduce new cloud components for persistence, monitoring, and security • You establish a resilient operations foundation with logging, observability, and CI/CD pipelines • You provision cloud resources consistently and maintainably using Infrastructure as Code • You prepare technical foundations for robust artifact management and controlled audit access • You make well-founded technical decisions and surface risks early • You drive larger topics pragmatically into viable implementations

• Iteratively design and develop scalable, resilient, and efficient platform solutions using cloud services and modern software design frameworks. • Create modular, reusable infrastructure using Terraform and related tools. • Partner interdepartmentally and cross-functionally to strengthen DevOps practices through automation. • Architect, develop, and maintain CI/CD pipelines for platform functions and customer needs at scale. • Deliver solutions based on event-driven, object-oriented, and functional design patterns. • Own the design and implementation of monitoring and logging solutions to ensure reliability and performance. • Support the definition and maintenance of a lifecycle process for platform systems to ensure updates, patches, and security compliance. • Apply deep expertise in platform engineering while leveraging cross-disciplinary knowledge to drive effective collaboration across development, operations, and security teams. • Resolve cloud infrastructure issues quickly, analyze performance, and implement improvements. • Analyze system performance and implement optimizations to improve speed, efficiency, and resource utilization. • Anticipate and plan infrastructure scaling to meet the growing needs of the business. • Apply a security-first approach, ensuring SOC, SOC2, HIPAA, NIST, and regulatory requirements are met. • Implement and manage IAM, firewalls, and SIEM solutions. • Integrate diverse and non-traditional systems to deliver practical, forward-thinking outcomes. • Navigate challenges creatively and find effective solutions when conventional approaches fall short. • Lead assigned projects, driving cross-team collaboration, execution delivery, and accountability. • Write clean, efficient, and well-documented code while following best practices and established development standards. • Take part in the on-call rotation to ensure the reliability and availability of platform services. • Use generative AI tools such as Claude, Copilot, or ChatGPT to improve productivity and deliver high-quality outcomes.

• Design, build, and maintain scalable ML, AI, and data-platform services that power production features end-to-end. • Operate LLM applications in production: chat with memory, retrieval, prompt management, versioning, and experimentation. • Build and run structured evaluation pipelines (golden datasets, regression checks) so changes ship with confidence. • Own AI/ML observability and monitoring instrument, trace, and debug model behavior in prod with tools like Langfuse or MLflow (knowing one is enough). • Integrate and serve LLM-agnostic model backends (e.g., Anthropic models via Bedrock and Vertex) and support deterministic/custom ML where open-source models fall short (e.g., document/PDF parsing). • Contribute to data lineage, governance, and compliance as the platform matures. • Partner closely with product and the broader team to move fast without breaking quality.