Consultant, Cybersecurity (Data Engineer)

If you’re passionate about being part of a dynamic organization that enables a Fortune 100 company with nearly $70 billion in annual sales to drive innovation and adopt new technologies that deliver business results, then Nationwide’s Technology team could be the place for you! At Nationwide®, “on your side” goes beyond just words. Our customers are at the center of everything we do and we’re looking for associates who are passionate about delivering extraordinary care.

This role will work a hybrid schedule coming into the Columbus, Ohio, Des Moines, Iowa, or Scottsdale, Arizona office 2 days (Mondays & Wednesdays) per week. Remote internal associates may also be considered.

The Cyber Security Operations Center’s Innovation and Integration (CSOC-INI) team transforms emerging ideas into secure, production-ready capabilities that advance cyber defense. Across agentic solutions, modern application engineering, platform foundations, and security data engineering, we translate complex operational needs into scalable, observable outcomes.

This is a high-impact opportunity to build the security data foundation that powers modern Cyber Security Operations. In this role, you’ll design and operate the pipelines, lakehouse data models, and telemetry engineering patterns that turn massive volumes of raw security data into trusted, usable intelligence for detections, analytics, and investigations. You’ll work across cyber security, detection engineering, platform, and data teams to ensure the security data lake is scalable, observable, governed, and ready to support mission-critical operational decisions.

Job description

Designs, builds, and operates the security data engineering foundation that enables telemetry ingestion, normalization, enrichment, analytics, and detections across the Cyber Security Operations Center.

Owns secure and reliable data pipelines, lakehouse data models, telemetry quality controls, and the engineering patterns needed to support security analytics, detection content, investigations, and downstream operational workflows.

Partners closely with cyber security professionals, detection engineers, platform and operations engineers, and data consumers to ensure the security data lake is trustworthy, observable, scalable, governed, and production-ready.

Requirements

• Security Operations experience preferred

• Strong data engineering skills, with advanced Databricks and Spark experience and strong working ability in Python and SQL

• Experience building and operating batch and streaming data pipelines for high-volume telemetry, logs, events, and security datasets

• Strong knowledge of telemetry normalization (OCSF), enrichment, schema design, and data quality controls across security-relevant data sources

• Experience designing and maintaining lakehouse or data lake architectures that support analytics, detections, investigations, and long-term retention needs

• Familiarity with detection engineering requirements, including how curated data supports rules, analytics, triage, and investigation workflows

• Experience integrating enterprise data sources, APIs, files, message streams, and cloud storage in a secure and maintainable way

• Working knowledge of observability, cost management, performance tuning, and operational support for production data platforms

• Ability to collaborate with architecture, platform, security, governance, and Cyber Security Operations stakeholders

• Excellent verbal and written communication skills

• Security Operations experience preferred

Expectations

• Build and maintain production-grade data pipelines and lakehouse data products that support telemetry analysis, detections, and cyber security investigations

• Define and implement ingestion, parsing, normalization, enrichment, and curation patterns for diverse security and platform telemetry sources

• Design trustworthy data models and storage patterns in Databricks that balance performance, cost, lineage, retention, and analytical usability

• Partner with detection engineers and analysts to ensure data products expose the fields, context, and quality needed for effective rules and analytics

• Implement data quality monitoring, freshness checks, reconciliation controls, and operational telemetry for critical data flows

• Optimize jobs, clusters, storage layout, and query performance for scale, reliability, and cost efficiency

• Support secure access patterns, governance controls, and auditability requirements for sensitive security data

• Troubleshoot pipeline defects, telemetry gaps, and downstream data issues quickly, and support production incident response when data engineering is involved

• Continuously improve reusable ingestion frameworks, data standards, and enginee