• Own the architecture of our data lakehouse—make build-vs-buy decisions, create technical designs, and lead implementation. • Write production Python code for critical data pipelines and infrastructure components (PySpark, Kafka, NiFi). • Set engineering standards for code quality, testing, observability, and documentation. • Mentor Data Engineers through code reviews, 1:1s, and technical guidance. • Partner with Product to translate requirements into scalable technical solutions. • Design data governance systems for lineage, cataloging, and compliance with government security standards.

• Rapidly validate new data initiatives end-to-end—without sacrificing adoptability. • Prototype representative connectors and pipelines (batch + streaming). • Generate credible performance/operability readouts. • Ship a handoff package that Foundation or a delivery team can productize. • Design “prototype-grade but adoptable” schemas and data models with clear semantics and evolution discipline. • Build incremental lakehouse datasets (Hudi/Iceberg/Delta patterns) and produce queryable outputs for realistic latency/throughput evaluation. • Bake in data quality and provenance mindset early (checks, metadata hooks, operability basics). • Containerize and deploy prototypes on Kubernetes; deliver minimal runbooks/configs that make adoption straightforward. • Produce adoption artifacts: schemas, reference implementations, technical design notes, and an integration backlog.

• Develop and maintain robust, scalable backend systems using Node.js (Express and NestJS). • Collaborate with engineers, designers, and product managers to drive projects forward. • Tune and optimize database queries for maximum efficiency and performance. • Optimize and improve existing code for better performance and user experience. • Troubleshoot and debug issues, ensuring smooth operations. • Share your knowledge and expertise to foster a culture of learning and growth.

• Design and implement robust search solutions that scale with our rapidly growing user base • Improve search relevance, accuracy, and speed to deliver the most relevant results to users at blazing fast speeds • Improve our real-time indexing pipelines to ensure search results remain up-to-date • Create measurement frameworks to evaluate and improve search quality • Build and enhance vector search capabilities to power next-generation search experiences • Collaborate with AI, backend, and product teams to integrate search into new features • Troubleshoot complex search-related issues at scale • Mentor junior engineers and contribute to engineering best practices