Data Platform Engineer

Primerica•Duluth, GA

About The 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.

Requirements

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

Nice To Haves

Advanced degree in Computer Science, Data Engineering, or related field preferred.
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.

Responsibilities

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.