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About This Role
Your work days are brighter here.
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.
Workday Pay Transparency Statement
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. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday’s comprehensive benefits, please click here .
Primary Location: USA.VA.Reston
Primary Location Base Pay Range: $159,600 USD \- $239,400 USD
Additional US Location(s) Base Pay Range: $144,400 USD \- $258,000 USD
Our Approach to Flexible Work
With Flex Work, we’re combining the best of both worlds: in\-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in\-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.
Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records.
Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.
At Workday, we are committed to providing an accessible and inclusive hiring experience where all candidates can fully demonstrate their skills. If you require assistance or an accommodation at any point, please email [email protected] .
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Salary Context
This $144K-$239K range is above the 75th percentile for Data Engineer roles in our dataset (median: $160K across 37 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Workday, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $208,300 based on 266 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($191K) sits 8% below the category median. Disclosed range: $144K to $239K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Workday AI Hiring
Workday has 6 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Positions span Reston, VA, US, Chicago, IL, US, Seattle, WA, US. Compensation range: $207K - $414K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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