Lead Data Engineer - AI Pipelines & MLOps

American Society of Clinical Oncology (ASCO); Alexandria (VA); Hybrid; USD 164,000 - 200,000; Full time

Benefits offered by this job

Open Leave Policy

Paid Family Leave

401(k): 7.5% Employer Contribution

Employee Assistance Program

Fertility and Family Forming

Healthcare Concierge

Flexible Spending Account(s)

Healthcare Savings Account

Disability and Life Insurance

Job summary

American Society of Clinical Oncology (ASCO) seeks a Lead Data Engineer to architect and implement scalable data pipelines for AI, analytics, and operations in a hybrid, 1–2 days onsite weekly model at our Alexandria, VA headquarters.

The role requires 9+ years building enterprise-grade data pipelines, 2+ years AI/ML production deployments, and deep Python/cloud experience. You will mentor engineers, establish standards, and drive cost-efficient, reliable data solutions across cloud platforms.

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Applied Mathematics, or a related technical field (or equivalent practical experience).
• 9+ years hands-on experience architecting, building, and maintaining enterprise-grade batch and streaming data pipelines in modern cloud environments.
• 2+ years 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 OpEx.
• Proven ability to drive technical strategy, establish coding standards, and mentor engineering teams through direct code contribution.

Responsibilities

• Architect, deploy scalable data pipelines and products in a multi-cloud ecosystem to unify enterprise data assets for AI, analytics, and operations.
• Design and optimize batch and streaming data ingestion pipelines for structured and unstructured data.
• Ensure security, quality, reliability, explainability, and maintainability of solutions.
• Build and operationalize AI capabilities including RAG, semantic search, vector databases, and autonomous multi-agent workflows.
• Establish MLOps and LLMOps rigor with CI/CD, containerization, and observability frameworks.
• Write production-grade Python code and microservices that integrate data products into AI agents and business applications.
• Optimize platform cost and performance by refining data models, storage tiers, and compute workloads.
• Translate strategy into scalable technical solutions in collaboration with product leads and stakeholders.
• Lead through code and mentorship, set engineering standards, and perform rigorous code reviews.
• Ensure operational reliability and incident health with root-cause analysis and rapid remediation.

Skills

Python

Data pipelines

MLOps

Cloud architecture

Mentoring

Code reviews

Education

Bachelor’s degree in Computer Science or related field

Master’s degree preferred

Tools

Docker

BigQuery

LLM integrations

Vector databases

CI/CD

Job description

American Society of Clinical Oncology (ASCO) seeks a Lead Data Engineer to architect and implement scalable data pipelines for AI, analytics, and operations in a hybrid, 1–2 days onsite weekly model at our Alexandria, VA headquarters.

The role requires 9+ years building enterprise-grade data pipelines, 2+ years AI/ML production deployments, and deep Python/cloud experience. You will mentor engineers, establish standards, and drive cost-efficient, reliable data solutions across cloud platforms.