• You will be responsible for owning and delivering quarterly goals for your team, leading engineers on your team through ambiguity to solve open-ended problems, and ensuring that everyone is supported throughout delivery. • You will support your peers and stakeholders in the product development lifecycle by collaborating with product management, design & analytics by participating in ideation, articulating technical constraints, and partnering on decisions that properly consider risks and trade-offs. • You will proactively identify project, process, technology, or business issues, advocate for them, and lead in solving them. • You will support the operations and availability of your team’s artifacts by creating and monitoring metrics, escalating when needed, and supporting “keep the lights on” & on-call efforts. • You will foster a culture of quality and ownership on your team by setting or improving code review and design standards for your team, and advocating for them beyond your team through your writing and tech talks. • You will help develop talent on your team by providing feedback and guidance, and leading by example.

• 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.