Information Security Engineer

Location: Irving, TX

Salary: $53.00 USD Hourly – $57.00 USD Hourly

We are seeking a Data Engineer to support our Identity and Access Management (IAM) Data Lake initiatives within the Information Security Engineering organization. In this contingent role, you will contribute to medium-complexity engineering efforts, participate in large-scale planning, and help develop secure, scalable data solutions on Google Cloud Platform
.

You will review and analyze technical challenges, recommend solutions, and collaborate with cross-functional partners across Information Security Engineering to meet delivery requirements. This role requires strong technical expertise, problem-solving skills, and the ability to work within established security, compliance, and engineering frameworks.

Location: Irving, TX (Preferred). Dallas, TX or Ohio are acceptable alternatives.

• Design, build, and maintain Data Lake solutions on Google Cloud Platform using modern big data tools and frameworks.
• Develop and optimize data ingestion pipelines (batch and streaming) leveraging Google Cloud Platform-native services
  .
• Analyze moderately complex information security engineering challenges and propose robust, scalable solutions.
• Implement data processing using Py Spark ,
  Airflow
  , APIs, and CI/CD workflows.
• Support data modeling, schema design, Avro/Parquet/ORC usage, and metadata strategies.
• Apply best practices in access control, bucket structuring, lifecycle management, and secure data architecture.
• Collaborate closely with internal partners to deliver high-quality engineering outcomes in alignment with security policies and compliance requirements.
• Troubleshoot data pipeline issues and contribute to continuous improvement efforts within the IAM Data Lake environment.

• 4 years of experience in Information Security Engineering, Data Engineering, or related technical fields (experience may be through work, consulting, training, military service, or education).
• Proven hands-on expertise with:
• Google Cloud Platform (4-6 years)
• PySpark (4-6 years)
• Data processing frameworks (4-6 years)
• Airflow (2-4 years)
• API development (2-4 years)
• Data pipelines (2-4 years)
• Hadoop ecosystem / HDFS (2-4 years)
• Data modeling principles (1-2 years)
• AVRO and other columnar formats

• Strong understanding of Google Cloud Platform architectural best practices, including:
• Bucket structure and naming standards
• Access control models
• Lifecycle management policies
• Expertise with Parquet
  , Avro
  , ORC
  , and compression strategies.
• Experience building batch and streaming pipelines using Google Cloud Platform services such as Dataflow, Pub/Sub, Cloud Storage, Big Query, and Composer.
• Knowledge of Pub/Sub-based streaming architecture
  , including schema management, evolution, and versioning.
• Familiarity with Change Data Capture (CDC) and incremental ingestion techniques.
• Understanding of downstream consumption patterns including APIs, materialized views, and curated analytical datasets.