Senior Director, Data Engineer

Drive enterprise data engineering leadership for identity analytics, real-time decisioning, and AI/ML security platform capabilities.

Responsibilities

• Own enterprise strategy and multi-year roadmap for identity analytics, access decisioning, and AI/ML security platforms, aligning AI/ML investment with business priorities plus audit and regulatory needs
• Partner with C-suite and CISO-org stakeholders to fund next-generation capabilities
• Lead and grow a 50+ person organization across Data Engineering, ML, Generative AI, and Security Products, including managers and senior individual contributors
• Hire, develop, and retain technical talent; set engineering standards and maintain delivery excellence
• Set technical direction hands-on
• Review architectures, model designs, and system trade-offs
• Stay close to code and data to make sound decisions under uncertainty and earn engineering credibility
• Architect low-latency, event-driven systems for real-time identity decisioning and threat detection using streaming telemetry, behavioral signals, and contextual graph data
• Design, build, and maintain robust ML infrastructure and pipelines for end-to-end workflows: feature extraction, training, testing, guardrails, evaluation, deployment, and both real-time and batch inference with performance, scalability, and reliability
• Advance MLOps practices with automated, metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring and observability for models in production
• Build the intelligence layer for agentic and non-human identity using behavioral analytics, ML-based intent determination, and context graphs to secure AI agents and workload identities
• Apply state-of-the-art LLM and GenAI in production for threat-intelligence summarization, automated incident triage, and adaptive access-policy recommendations; introduce optimization techniques to improve scalability, cost, latency, and throughput
• Operate automated governance with pipelines from enterprise and IGA platforms, operationalizing NIST controls with continuous control validation and audit-ready evidence
• Lead enterprise readiness for emerging cyber risk from frontier AI models and agentic attack patterns

Requirements

• Bachelor’s Degree in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• 9+ years of experience in data engineering
• 7+ years of people management experience
• 7+ years of programming experience with at least one of: Python, Java, Scala
• 5+ years driving technical delivery of roadmap features
• 6+ years designing and developing data pipelines
• 4+ years in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Preferred Qualifications

• Master’s Degree in Computer Science or a related field
• 12+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java
• 8+ years hands-on experience designing, deploying, and operating data workloads in at least one public cloud environment (AWS, Microsoft Azure, Google Cloud)
• 8+ years experience building or supporting distributed data or compute workloads using EMR, Spark, Glue, or Databricks
• 8+ years experience designing, implementing, and operating real-time or streaming data pipelines
• 6+ years experience in data observability (e.g., Monte Carlo, Splunk) or data orchestration tools (e.g., Airflow, Dagster)
• 8+ years experience working with unstructured or semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
• 8+ years experience designing and supporting data warehousing solutions (e.g., Snowflake, Redshift)
• 6+ years experience working in an Agile development environment
• 6+ years experience developing user-centric reusable data products
• 5+ years experience in Data Governance, Data Governance Platforms, Data Standardization, and Data Modeling

Technologies

• Python, Java, Scala
• NIST
• AWS, Microsoft Azure, Google Cloud
• EMR, Spark, Glue, Databricks
• Airflow, Dagster
• Monte Carlo, Splunk
• MongoDB, Cassandra, DynamoDB
• Snowflake, Redshift, SQL, NoSQL
• Generative AI, LLMs, GenAI

Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being
• Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

Location and Salary

• McLean, VA (onsite): USD 314,800 - 359,300 per year
• New York, NY: USD 343,400 - 392,000 per year
• Plano, TX: USD 286,200 - 326,700 per year
• Richmond, VA: USD 286,200 - 326,700 per year
• Pay range for other locations may differ; actual annualized salary offered at hire is reflected in the candidate’s offer letter