Cybersecurity Data Engineer: Lakehouse & Telemetry

Nationwide; Scottsdale (AZ); Hybrid; USD 118,000 - 222,000; Full time

Benefits offered by this job

Hybrid work schedule

Competitive benefits

Job summary

Nationwide Technology seeks a Cyber Operations professional to design and operate the security data engineering foundation. You will build telemetry ingestion, normalization, and analytics pipelines powering detections and investigations in a scalable data lake.

You will partner with cyber security, detection engineering, and platform teams to ensure the data lake is observable, governed, and production-ready. Hybrid work and strong collaboration are core to success.

Qualifications

• Strong data engineering skills with Databricks and Spark experience.
• Proficient in Python and SQL with production-grade data pipelines.
• Experience with batch and streaming data pipelines for telemetry and security data.
• Knowledge of telemetry normalization, data quality, and lakehouse patterns.
• Experience designing scalable data lake architectures for analytics and detections.

Responsibilities

• Designs, builds, and operates the security data engineering foundation for telemetry, analytics, and detections.
• Owns secure data pipelines and lakehouse data models to support crime analytics and investigations.
• Collaborates with cyber security, detection, platform, and data teams to ensure a trusted data lake.

Skills

Databricks

Spark

Python

SQL

Batch & streaming pipelines

Telemetry normalization (OCSF)

Lakehouse architectures

Detection engineering

Cloud data integration

Communication skills

Education

Bachelor’s degree in cyber security / MIS / engineering / CS / data analytics or related field

Job description

Nationwide Technology seeks a Cyber Operations professional to design and operate the security data engineering foundation. You will build telemetry ingestion, normalization, and analytics pipelines powering detections and investigations in a scalable data lake.

You will partner with cyber security, detection engineering, and platform teams to ensure the data lake is observable, governed, and production-ready. Hybrid work and strong collaboration are core to success.