Lead Data Engineer

American Society of Clinical Oncology•Alexandria, VA

1d•Hybrid

About The Position

ASCO is in search of a high-performing Lead Data Engineer to play a vital role as a hands-on architect and engineer turning complex data into strategic business value. This role is responsible for actively writing code, building scalable data pipelines, pioneering responsible AI practices, and deploying production-grade solutions for AI/ML, BI, and operations. The position will solve data engineering challenges, elevate technical standards, and lead through code. This position is hybrid with a primary location at our headquarters in Alexandria, VA. The hire must reside within 75 miles of our headquarters. We anticipate the hire to be onsite approximately 1-2 days per week.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related technical field (or equivalent practical experience).
9+ years of hands-on experience architecting, building, and maintaining enterprise-grade batch and streaming data pipelines in modern cloud environments.
2+ years of hands-on experience building and deploying production-grade AI/ML solutions, including RAG architectures, vector databases, LLM integrations, and agentic workflows.
Deep mastery of Python and modern data/software engineering practices.
Proven expertise with cloud-native data architectures (GCP/BigQuery, AWS, or Azure), distributed computing, enterprise data modeling, and visualizing end-to-end integration flows.
Direct experience establishing MLOps/LLMOps pipelines, containerization (Docker), real-time evaluation frameworks, and automated CI/CD deployments.
Demonstrated ability to tune physical data models and compute workloads to increase system performance while controlling cloud operating costs (OpEx).
Proven ability to drive technical strategy, establish coding standards, and mentor engineering teams through direct, active code contribution.

Nice To Haves

Hands-on exposure to specialized AI orchestration tooling (e.g., Google Vertex AI Agent Builder).
Master’s degree in Computer Science, Data Science, or Artificial Intelligence.

Responsibilities

Design, build, and optimize production-grade batch and streaming data pipelines in a multi-cloud (GCP, AWS, Azure) ecosystem to unify enterprise data assets into a high-performance AI, analytics, and operational foundation.
Design, build, and optimize scalable batch and streaming data ingestion pipelines for both structured and unstructured data.
Ensure all developed solutions meet high standards for security, quality, reliability, explainability, and maintainability.
Build and operationalize modern AI capabilities—including RAG, semantic search, vector databases, and autonomous multi-agent workflows, grounded in clean enterprise context.
Build CI/CD, containerization, and observability frameworks to streamline LLM service integrations for fast, reliable, and cost-effective application delivery.
Deliver clean, maintainable Python code and microservices that integrate data products directly into AI agents, business applications, and operational workflows.
Continually refine database designs, storage tiers, and compute workloads to maximize query speed while actively optimizing cloud operating costs (OpEx).
Partner with product leads and business stakeholders to translate mission goals into scalable technical solutions that accelerate the digital roadmap.
Set engineering standards, drive rigorous code reviews, and elevate team capability by actively building alongside junior and mid-level data engineers.
Perform root-cause analysis and rapid remediation for complex data platform incidents to maintain continuous system availability and data integrity.