Senior Cybersecurity Data Engineer - AI/ML SME

About the role

We are seeking a highly specialized Senior Data Engineer - Cybersecurity to serve as the Subject Matter Expert (SME) for AI/ML and Platform Integration. This critical role sits at the intersection of core data platform infrastructure, advanced analytics, and external system integrations.

Responsibilities

• AI/ML Data Infrastructure & Tooling: Design, provision, and maintain the platform infrastructure required for end-to-end machine learning lifecycles. Optimize the platform for distributed training, model evaluation, and batch/real-time inference.
• Enterprise Feature Store Architecture: Design and manage the enterprise Feature Store. Ensure consistent, low-latency feature delivery, preventing data leakage between training pipelines and real-time production inference.
• Vector Infrastructure for GenAI: Architect and maintain vector databases and indexing pipelines required to support Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) patterns, and semantic search.
• Platform Integration & API Management: Serve as the SME for how external applications interact with the data lakehouse. Design, build, and secure high-throughput APIs, data connectors, and reverse-ETL patterns to sync data back into business systems (e.g., CRMs, ERPs, marketing automation).
• MLOps Collaboration & Automation: Partner closely with Data Scientists and MLOps teams to establish CI/CD automation for ML (MLOps). Transition experimental, unoptimized data science notebooks into resilient, production-grade automated workflows.
• Compute Optimization for Data Science: Configure and optimize compute engines tailored for heavy mathematical and data science workloads (e.g., Ray, Spark/EMR GPU instances).

Requirements

Basic Qualification Experience: 5+ years of data engineering experience, with at least 2+ years dedicated to supporting machine learning platforms, MLOps, or complex platform integrations.

ML Data Stack: Deep hands-on experience with AWS SageMaker, MLflow, or equivalent cloud-native ML platforms.

Feature Stores & Vector DBs: Proven experience implementing feature store frameworks (e.g., Feast, SageMaker Feature Store) and vector databases (e.g., Pinecone, Milvus, Qdrant, or Pgvector).

Distributed Compute & ML Libraries: Strong experience using Apache Spark / AWS EMR, Ray, or Dask to process massive datasets for feature extraction and model preparation.

Integration Patterns: Expert knowledge of building rest APIs, Webhooks, and utilizing streaming tools (e.g., AWS Kinesis, Kafka) for real-time integration.

Languages & CI/CD: Advanced proficiency in Python (including ML ecosystems like Pandas, NumPy, Scikit-Learn) and SQL. Extensive experience with GitHub Actions, GitLab CI, or Jenkins for data/ML pipelines.

Other Qualifications: Experience deploying and fine-tuning open-source LLMs or orchestrating AI agents using frameworks like LangChain or LlamaIndex. Experience with reverse-ETL tools (e.g., Census, Hightouch) or enterprise integration platforms.

Qualifications

Advanced degree in Computer Science, Data Science, or related field preferred.

Proven track record of delivering high-quality, scalable, and reliable data engineering solutions.

Strong problem-solving and analytical skills, with a passion for innovation and continuous improvement.

Excellent communication and collaboration skills, with the ability to work effectively in a fast-paced, dynamic environment.

Experience with cloud-native technologies such as AWS, Azure, or Google Cloud.

Knowledge of cybersecurity principles and practices, with a focus on data protection and compliance.

Ability to work independently and as part of a team, with a proactive and results-oriented mindset.

Skills

• Python, SQL, and other programming languages
• Apache Spark, AWS EMR, Ray, Dask
• AWS SageMaker, MLflow, Feast, SageMaker Feature Store
• Pinecone, Milvus, Qdrant, Pgvector
• REST APIs, Webhooks, Kafka, Kinesis
• CI/CD tools (GitHub Actions, GitLab CI, Jenkins)
• Open-source LLMs, LangChain, LlamaIndex
• Reverse-ETL tools (Census, Hightouch)
• Cloud-native technologies (AWS, Azure, Google Cloud)
• Data protection and compliance principles

Benefits

Flexible work arrangements, including the option to work remotely or in the office at least half of the time each quarter.

Comprehensive benefits package, including health insurance, retirement plans, and paid time off.

Opportunities for professional development and growth within the organization.

Workplace culture focused on inclusivity, diversity, and employee well-being.

Company commitment to sustainability and environmental responsibility.

Pay

The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants.

Primary Location: USA. VA. Reston
Base Pay Range: $159,600 USD - $239,400 USD

Additional US Location(s):
Base Pay Range: $144,400 USD - $258,000 USD

For more information regarding Workday’s comprehensive benefits, please click here.

Schedule

Flexible work arrangements, with the option to work remotely or in the office at least half of the time each quarter.

Application Deadline

The application deadline for this role is the same as the posting end date stated as below: 07/31/2026

Equal Opportunity Employer

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

Recruiter Contact Information

If you are being referred to one of our roles, ask your connection at Workday about our Employee Referral process!