Senior Cybersecurity Data Engineer - Data Platform & Lakehouse SME

Workday•Reston, VA

3d•$144,400 - $258,000•Hybrid

About The Position

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. We are seeking a powerhouse Senior Data Engineer to serve as the Subject Matter Expert (SME) for Data Platform and Lakehouse Architecture. In this role, you will be the foundational architect of our data universe. While other teams focus on moving data through pipelines or building machine learning models, your mission is to build, secure, and optimize the actual bedrock—the data lake, storage layers, and compute infrastructure—that powers our entire security operations and beyond. As the Platform SME, you will define how data is physically stored, partitioned, and accessed. You will establish the frameworks, compliance guardrails, and compute engine standards that allow the Data Logistics and AI/ML teams to build their workloads safely, rapidly, and at massive scale.

Requirements

5+ years of deep experience in data platform engineering, cloud infrastructure engineering, or data architecture, with a proven track record of designing large-scale enterprise data lakes.
Expert-level knowledge of open-table formats (Apache Iceberg is highly preferred, or Delta Lake) and deep understanding of file format internals (Parquet, ORC, metadata layers).
Advanced, production-level expertise across the AWS data stack, specifically AWS EMR (Spark/Presto/Trino tuning), AWS Athena, S3 infrastructure, and AWS Lake Formation.
Advanced proficiency with Terraform or AWS CDK for provisioning secure, multi-environment data infrastructures.
Deep understanding of modern cloud data warehouses (Snowflake, Databricks, or AWS Redshift) and cluster sizing/workload management.
Advanced proficiency in SQL (performance tuning, query optimization) and Python or Bash for infrastructure automation.
AWS Certified Solutions Architect – Professional or AWS Certified Data Engineer.
Experience setting up multi-region data lakes, cross-account data sharing, or data mesh architectures.
Experience with open-source query engines like Trino or StarRocks.

Responsibilities

Lead the design, infrastructure implementation, and evolution of our enterprise Data Lake/Lakehouse ecosystem on AWS.
Serve as the ultimate authority on modern open-table formats (Apache Iceberg or Delta Lake). Define the standards for partition strategies, schema evolution, file compaction, retention, and storage tiering to maximize performance and slash storage costs.
Design, configure, and maintain the foundational compute engines and query layers (e.g., AWS EMR clusters, AWS Athena, Redshift) utilized by downstream data engineers, analysts, and BI tools.
Define the foundational infrastructure and access patterns for enterprise Data Marts. Ensure underlying engines are optimized to handle heavy, concurrent query loads from downstream BI and reporting tools.
Architect and enforce global data governance, network isolation, encryption (at rest and in transit), and fine-grained access controls using AWS Lake Formation and AWS IAM.
Treat all platform infrastructure as software. Provision, deploy, and version-control the entire data platform environment using automated, reproducible code.
Proactively monitor, alert, and optimize AWS data spend. Perform capacity planning to ensure the platform scales seamlessly with data volume growth.