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Cybersecurity Data Engineer Jobs in Lancaster, PA

The OT Data Engineer - Unified Namespace is responsible for designing and implementing scalable ... Understanding of OT cybersecurity Key Capabilities: * Strong analytical and system design skills

As a Penske Systems Engineer - Cyber Security you will maintain network, server and workstation ... data systems to safeguard company information • Provide security reviews and define security ...

As a Penske Systems Engineer - Cyber Security you will maintain network, server and workstation ... of data systems to safeguard company information Provide security reviews and define security ...

Senior Systems Engineer - Cyber Security Summary Statement: You will be working with a team of ... Encrypt data transmissions and configure/maintain firewalls to conceal confidential information as ...

About the Role We are seeking a Software Engineer, Cybersecurity with strong experience in Elastic ... Build and optimize data ingestion pipelines for logs, events, and telemetry from multiple sources.

Senior Security Engineer

Lititz, PA · On-site

$106K - $145K/yr

This person will work closely with the VP, Cybersecurity, Security Analysts, IT Infrastructure ... Improve logging, alerting, and visibility across critical systems and data. * Work with internal ...

Senior Security Engineer

Lititz, PA · Remote

$106K - $145K/yr

This person will work closely with the VP, Cybersecurity, Security Analysts, IT Infrastructure ... Improve logging, alerting, and visibility across critical systems and data. * Work with internal ...

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Showing results 1-20

Cybersecurity Data Engineer information

See Lancaster, PA salary details

$43.2K

$126.1K

$172.5K

How much do cybersecurity data engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for cybersecurity data engineer in Lancaster, PA is $126,064.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,300.00 and $133,600.00 per year, depending on experience, location, and employer.

What does a Cybersecurity Data Engineer do?

A Cybersecurity Data Engineer is responsible for designing, building, and maintaining systems that collect, process, and analyze security-related data. Their main goal is to help organizations detect and respond to cyber threats by ensuring that data pipelines and storage solutions are secure and efficient. They often work with large datasets, security tools, and machine learning algorithms to identify vulnerabilities and unusual activity. Additionally, they collaborate with other IT and security professionals to implement best practices and enhance overall cybersecurity posture.

What are the key skills and qualifications needed to thrive as a Cybersecurity Data Engineer?

To thrive as a Cybersecurity Data Engineer, you need strong skills in data engineering, cybersecurity best practices, and programming languages such as Python or SQL, typically supported by a degree in computer science or a related field. Familiarity with security information and event management (SIEM) systems, big data tools like Hadoop or Spark, and certifications such as CISSP or CEH are highly valuable. Analytical thinking, problem-solving abilities, and effective communication set standout professionals apart in this role. These skills are crucial for designing secure data pipelines, detecting threats, and ensuring organizational data integrity.

How does a Cybersecurity Data Engineer typically collaborate with security analysts and IT teams?

Cybersecurity Data Engineers work closely with security analysts and IT teams to design, implement, and maintain data pipelines that support threat detection and incident response. They collaborate by integrating various data sources, ensuring data quality, and providing timely access to relevant information for analysis. Frequent communication and regular meetings are common to align on project requirements, prioritize tasks, and troubleshoot issues together. This collaborative approach ensures that security teams have the accurate, actionable data they need to protect organizational assets effectively.

What is the difference between Cybersecurity Data Engineer vs Cybersecurity Analyst?

AspectCybersecurity Data EngineerCybersecurity Analyst
Required CertificationsCompTIA Security+, CISSP, CEHCompTIA Security+, CISSP, CEH
Work EnvironmentData-focused, engineering teams, IT departmentsSecurity operations centers, incident response teams
Employer & Industry UsageTech companies, finance, healthcareGovernment agencies, corporations, cybersecurity firms
Common Search & ComparisonYesYes

While both roles require cybersecurity certifications and work within security-focused environments, Cybersecurity Data Engineers primarily develop and manage data infrastructure for security analytics, whereas Cybersecurity Analysts focus on monitoring, threat detection, and incident response. Understanding these differences helps organizations assign the right skills to their security teams.

What are popular job titles related to Cybersecurity Data Engineer jobs in Lancaster, PA?

For Cybersecurity Data Engineer jobs in Lancaster, PA, the most frequently searched job titles are:

What cities near Lancaster, PA are hiring for Cybersecurity Data Engineer jobs?

Cities near Lancaster, PA with the most Cybersecurity Data Engineer job openings:

Enterprise Data Platform Architect / Engineering Lead - 26-10303

Compu-Vision - IT

York, PA • On-site

Contractor

Posted 6 days ago


Job description

Enterprise Data Platform Architect / Engineering Lead

Location: Harrisburg, PA — Hybrid (1 day onsite per week)
Duration: Contract

Position Overview

We are seeking an experienced Enterprise Data Platform Architect / Engineering Lead to provide technical leadership for the implementation and continuous evolution of enterprise data platforms.

This is a hands-on technical leadership role focused on designing, engineering, and delivering reusable, secure, cloud-based data products and shared platform services that enable multiple organizations to develop and deploy modern digital services, analytics, and AI solutions.

The successful candidate will balance enterprise technology strategy with practical execution, working closely with solution architects, data engineering teams, cloud engineering, cybersecurity, governance, and other technology stakeholders.

Key ResponsibilitiesEnterprise Platform Architecture
  • Define and evolve target architecture for an enterprise data platform.
  • Develop multi-year technical roadmaps aligned with enterprise modernization initiatives.
  • Evaluate emerging technologies while maintaining architectural consistency and standards.
  • Establish reusable architecture patterns and platform capabilities.
Platform Engineering
  • Design, build, and continuously enhance a modern cloud-based data platform.
  • Establish engineering standards, operational practices, and technical guardrails.
  • Design secure multi-tenant environments supporting multiple organizations and data domains.
  • Address data isolation, data-sharing, and data co-mingling considerations.
  • Partner with cybersecurity teams to implement IAM, encryption, auditing, and other security controls.
  • Develop infrastructure and platform services that can be consumed across multiple teams.
Enterprise Enablement
  • Design and build reusable platform services rather than organization-specific solutions.
  • Develop standardized onboarding patterns for teams adopting enterprise data products and services.
  • Support enterprise modernization initiatives through standardized technology capabilities.
  • Develop data products and infrastructure capable of supporting AI and advanced analytics workloads.
  • Promote API-first and product-oriented approaches to enterprise information sharing.
Leadership & Collaboration
  • Establish and promote engineering best practices and development standards.
  • Collaborate with technology leaders and engineering teams across the organization.
  • Partner with cloud, integration, security, governance, and data engineering teams.
  • Provide technical guidance and influence architecture decisions without direct managerial authority.
  • Communicate complex technology concepts effectively to both technical and non-technical audiences.
Required Qualifications & Experience
  • Bachelor's degree in Computer Science, Information Systems, Systems Programming, or a related field, or an equivalent combination of education and relevant experience.
  • 10+ years of experience building and supporting large-scale cloud data platforms.
  • 7+ years of hands-on experience in modern data architecture, platform engineering, and cloud infrastructure.
  • Strong experience designing and implementing enterprise cloud data platforms.
  • Demonstrated experience implementing cloud security controls, including:
    • IAM
    • RBAC
    • Encryption
    • Audit logging
    • Security governance
  • Proven experience with Infrastructure as Code, including Terraform or an equivalent technology.
  • Strong understanding of modern DevSecOps practices and shift-left security principles.
  • Experience partnering with cloud engineering, cybersecurity, CISO, and governance/risk/compliance teams.
  • Experience designing reusable platform capabilities for multiple teams or business units.
  • Strong understanding of enterprise data architecture and cloud-native technologies.
Technical Skills

Experience with one or more of the following technologies is highly desirable:

  • Snowflake
  • Databricks
  • AWS
  • Microsoft Azure
  • Cloud data platforms
  • Data engineering platforms
  • Containerized applications
  • Terraform / Infrastructure as Code
  • DevSecOps
  • IAM / RBAC
  • Cloud security
  • Data governance
  • API-first architectures
  • Enterprise data products
  • AI-ready data infrastructure
Preferred Background
  • Experience building enterprise-scale data platforms supporting multiple teams or business units.
  • Experience developing shared services and reusable technology patterns.
  • Experience with multi-tenant cloud architectures.
  • Experience supporting AI/ML and advanced analytics workloads.
  • Experience with cloud-native data engineering and platform automation.
  • Experience working within highly regulated or large enterprise environments.
  • Experience establishing engineering standards and platform governance.