Data Platform Engineer

Join Our Team

In 2025, USA Today recognized Primerica as a Top Workplace USA for the fifth year in a row, and Newsweek named Primerica one of America’s Greatest Workplaces for Diversity for the second consecutive year. In 2024, the Atlanta Journal-Constitution named Primerica as a Top Workplace for the eleventh consecutive year, and Forbes recognized Primerica as one of America’s Best Employers for Women for the fifth year in a row. In addition, for the tenth time Primerica has been voted a Best Employer by Gwinnett Magazine. Primerica is a great place to work! Join our team to experience what it’s like to work at “one of the best places to work in the metro Atlanta”.

About this Position

The Data Platform Engineer is responsible for designing, building, and operating scalable, secure, and reliable data platform infrastructure that supports modern data and analytics workloads. This role works within the Cloud Engineering organization to enable data modernization efforts by developing automated, cloud-aligned data platform capabilities that support application teams, analytics initiatives, and enterprise data services. The position focuses on improving the reliability, observability, performance, and operational efficiency of data platforms while ensuring strong security, governance, and integration with existing infrastructure and DevOps practices. The Data Platform Engineer collaborates closely with cloud engineers, application teams, and data stakeholders to deliver resilient data platform services that support the organization’s growing demand for modern data capabilities.

The salary range for this role is $120k - $140K.
This role is not eligible for sponsorship now or in the future.

Responsibilities & Qualifications

The role of a Data Platform Engineer is a senior-level position that bridges the gap between traditional infrastructure engineering and data engineering, combining deep technical knowledge of distributed systems and cloud technologies with an understanding of data, analytics, and machine learning workflows and requirements. They design, build, and manage the data and analytics ecosystem ensuring it is reliable, scalable, secure, and cost effective, enabling analysts, data scientists, and engineers to work efficiently with organizational data assets. Their responsibilities typically include:

• Collaboration and Enablement : Serve as technical advisor for data team members across the organization, providing guidance on data ecosystem capabilities and best practices. Create documentation and provide self-service tools and platforms that empower data engineers, domain experts, and data scientists to work more independently. Collaborate with stakeholders to understand business requirements and translate them into technical solutions. Collaborate with software engineers to integrate data platforms and machine learning models with applications.
  
  
  
  
• Infrastructure Architecture and Design: Lead the design and architecture of comprehensive data infrastructure solutions that meet current needs while anticipating future growth. Evaluate and select appropriate tools and platforms for data exploration, processing, and storage, analytics and machine learning model development, dashboarding, and predictive model inference. Ensure infrastructure scalability while designing for high availability and disaster recovery.
  
  
  
  
• Data Pipeline Management: Collaborate with stakeholders to design and build data pipelines using modern data engineering tools and frameworks that enable DevOps principles to be applied to the data lifecycle.
  
  
  
  
• Data Infrastructure and Platform Management: Build and configure distributed cloud-based infrastructure and platforms for data storage, exploration, and processing, machine learning model development, training, and serving. This includes data lakes and platforms such as AWS Sagemaker. Build and manage infrastructure for business intelligence platforms such as Power BI and Tableau. Develop infrastructure as code solutions to ensure reproducible and version-controlled infrastructure deployments.
  
  
  
  
• Data Quality and Governance: Work with stakeholders to establish data quality frameworks and automated data validation processes. Build and manage data cataloguing and metadata management systems. Design and implement data governance policies and procedures. Ensure data security, privacy and compliance with relevant regulations.
  
  
  
  
• Monitoring and Performance Optimization: Implement advanced monitoring and observability solutions to track the performance and health of infrastructure and data pipelines. Analyze system metrics, logs, and alerts to identify platform issues and performance bottlenecks. Implement measures to prevent recurrence and optimize resource utilization.
  
  
  
  
• Cost Management and Optimization: Implement cloud architecture best practices to build cost effective solutions. Analyze usage patterns and costs, identifying opportunities for optimization through reserved instances, spot instances, or architectural changes.
  
  
  
  
• Incident Response and Problem Resolution: Provide technical expertise in troubleshooting and resolving complex incidents and problems related to infrastructure and data or analytics pipeline issues. Conduct root cause analysis, implementing preventive measures, and driving process improvements.
  
  
  
  
• Technology Evaluation and Innovation: Stay current with evolving data technologies and continuously evaluate new tools, frameworks, and services that could enhance the organization’s data capabilities. Undertake proof of concept projects, benchmarking performance, and assessing total cost of ownership, accounting for tradeoffs and value of technology adoption.

The role of Data Platform Engineer represents a critical intersection of infrastructure engineering, data management, and business enablement. It is essential to the organization’s data strategy and success, supporting all data-intensive operations and data-driven initiatives along with the insights and business value they bring.

Education

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related technical discipline, or equivalent professional experience.
• Advanced degree in Computer Science, Data Engineering, or related field preferred.

Experience

• 5–8+ years of experience in infrastructure engineering, cloud engineering, data engineering, or platform engineering roles.
• Proven experience designing and operating scalable data platforms and distributed systems in enterprise environments.
• Experience supporting analytics, data science, or machine learning workloads in production environments.
• Demonstrated experience implementing Infrastructure as Code and automation for platform provisioning and operations.

Technical Knowledge and Skills

• Strong understanding of distributed systems, data platform architectures, and modern cloud-based infrastructure.
• Experience with cloud platforms and services used for data processing and analytics (e.g., AWS, Azure, or Google Cloud).
• Proficiency in building and maintaining data pipelines using modern data engineering frameworks and tools.
• Experience with containerized environments and orchestration platforms (e.g., Kubernetes) and modern DevOps practices.
• Knowledge of data storage technologies including relational databases, data lakes, and distributed data platforms.
• Experience with monitoring, observability, and performance optimization for data infrastructure and pipelines.
• Familiarity with infrastructure automation tools and scripting languages (e.g., Terraform, Ansible, Python, or similar).
• Understanding of data governance, security, privacy, and compliance considerations for enterprise data environments.

Professional Skills

• Ability to translate business and analytics requirements into scalable technical platform solutions.
• Strong troubleshooting and problem-solving skills in complex, distributed systems environments.
• Ability to collaborate effectively with cloud engineers, software engineers, data engineers, analysts, and business stakeholders.
• Strong documentation, communication, and technical advisory skills.

Preferred Qualifications

• Experience supporting enterprise analytics or machine learning platforms.
• Experience implementing data governance frameworks and metadata management solutions.
• Familiarity with business intelligence platforms and enterprise data visualization tools.
• Experience optimizing cloud infrastructure costs for large-scale data workloads.

FLSA status:
This position is exempt (not eligible for overtime pay):

Yes

Our Benefits:

• Day one health, dental, and vision insurance

• 401(k) Plan with competitive employer match

• Vacation, sick, holiday and volunteer time off

• Life and disability insurance

• Flexible Spending Account & Health Savings Account

• Professional development

• Tuition reimbursement

• Company-sponsored social and philanthropy events

It has been and will continue to be the policy of Primerica, Inc., and its subsidiaries to be an Equal Opportunity Employer. We provide equal opportunity to all qualified individuals regardless of race, sex, color, religious creed, religion, national origin, citizenship status, age, disability, pregnancy, ancestry, military service or veteran status, genetic or carrier status, marital status, sexual orientation, or any classification protected by applicable federal, state or local laws.

At Primerica, we believe that diversity and inclusion are critical to our future and our mission – creating a foundation for a creative workplace that leads to innovation, growth, and profitability. Through a variety of programs and initiatives, we invest in each employee, seeking to ensure that our people are not only respected as individuals, but also truly valued for their unique perspectives.