Senior Cybersecurity Data Engineer - AI/ML SME

Senior Data Engineer - Cybersecurity

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We're obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we're shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you'll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We're in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you'll do meaningful work with Workmates who've got your back. In return, we'll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you've found a match in Workday, and we hope to be a match for you too.

About the Team

We are a newly formed, forward-looking Cybersecurity Data Engineering & Enablement Team driving the future of our enterprise defense strategy. Our mission is to build a next-generation, centralized data lakehouse that unifies all security telemetry into a single, high-performance ecosystem. Operating across two specialized verticals—Data Engineering (ingestion, enrichment, and semantic layers) and Data Platform (foundational infrastructure, security architecture, and AI enablement)—we are designing a scalable, cloud-native foundation from the ground up. By combining cutting-edge data architecture with advanced analytics, we empower our threat hunters, data scientists, and incident responders with the real-time, trusted intelligence needed to protect the enterprise at scale.

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. Your primary mission is to optimize our data platform to serve as a high-performance engine for Data Science, Machine Learning (ML), and Generative AI (GenAI) workloads.

Additionally, you will own the integration fabric of the platform—building the robust APIs, webhook ingestion engines, and data connectors that seamlessly sync our central lakehouse with downstream business applications, SaaS platforms, and third-party ecosystems.

Key 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).

About You

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.