Job Description

Data Platform Engineer

Location: Dallas Texas; onsite-hybrid model

Salary: $130-165k / year - DOE

Benefits: This position is eligible for Medical, Dental, Vision, and 401(k) benefits

The Data Platform Engineer role combines traditional database administration with modern data platform and solution engineering. This role is responsible for both the day-to-day operation of critical on-premises and cloud data platforms, and the design and delivery of scalable, cloud native data solutions that enable analytics, integration, virtualization, and real-time data access.

The Data Platform Engineer owns the full lifecycle of the data platform , from designing and deploying databases and data services through ongoing operational management. This includes reliability, performance, cost optimization, workload balancing, resiliency, capacity planning, and continuous modernization.

Success in this role requires the ability to support core operational DBA responsibilities while simultaneously designing and evolving modern data platforms, pipelines, and integrations that meet changing business needs. We are in the midst of a large-scale migration to a new infrastructure platform, and this role will be critical to ensure the successful deployment of new SQL Server environments supporting all non-SAP applications and reporting interfaces. As the primary DBA for the team, you will also need to be able to coordinate with onshore/offshore services groups, as needed.

Key Responsibilities:

Platform & Database Operations

• Design, deploy, and manage relational and analytical data platforms across on-premises and cloud environments.
• Support day-to-day database administration activities, including backups, restores, patching, upgrades, capacity planning, and operational health.
• Implement database configuration standards and manage environments throughout the development and production lifecycle.

Reliability, Availability & Recovery

• Design and maintain redundancy, resiliency, and recovery strategies, including high availability and disaster recovery.
• Ensure platforms meet service level objectives for availability and recoverability.

Performance, Cost & Workload Management

• Monitor and optimize database and data platform performance, identifying and mitigating risks related to latency, throughput, workload contention, or cost escalation.
• Apply performance strategies such as indexing, partitioning, data layout optimization, and query tuning.
• Balance workloads across platforms to ensure efficient resource utilization and predictable performance.

Data Platform Solutions & Integration

• Design and implement data platforms that support analytics, reporting, data virtualization, mirroring, and real-time or near real-time data access.
• Build and maintain data pipelines and integrations.
• Support integration patterns including APIs, event-based interfaces, and service-oriented architectures.

Access & Governance Enablement

• Design and implement governed data access models that provide secure, controlled access to data while enabling self-service analytics.
• Manage user and service access changes as needed and collaborate with security and risk teams to align with enterprise policies.
• Ensure authentication, authorization, and auditing mechanisms are applied consistently across data platforms.

Monitoring, Operations & Support

• Implement monitoring, alerting, and logging for databases and data platforms to proactively detect and resolve issues.
• Troubleshoot and resolve high priority incidents with a focus on root cause analysis and long-term stability.
• Provide operational support for critical data infrastructure with a strong bias toward automation and prevention.

Automation & DevOps Practices

• Apply DevOps and infrastructure as code principles to data platforms to enable repeatable, automated deployments.
• Ensure changes are versioned, tested, and promoted through environments using standardized release practices.

Documentation & Knowledge Sharing

• Maintain clear documentation of platform architecture, configurations, deployment standards, and operational procedures.
• Use SharePoint and DevOps to support transparency, traceability, and knowledge reuse.

Collaboration & Innovation

• Partner with application teams, data architects, analysts, infrastructure engineers, and security teams to deliver reliable data solutions.
• Continuously evaluate and adopt new data platform capabilities that improve scalability, reliability, and developer productivity.

Required Qualifications

• Bachelor’s degree in Computer Science (or a related field plus equivalent experience).
• 5+ years of experience supporting and engineering data platforms in on-premises and/or cloud environments.
• Strong experience with relational database platforms and database administration fundamentals.
• Experience with performance tuning, high availability, backup and recovery, and capacity planning.
• Working knowledge of cloud-based data platforms and managing database services.
• Proficiency in SQL and at least one general purpose programming or scripting language.
• Extensive experience with SQL Server and Azure SQL.
• Strong written and verbal communication skills.

Preferred Qualifications

• Experience supporting analytical and reporting platforms.
• Experience designing and supporting data pipelines, integrations, and analytical workloads.
• Experience integrating enterprise systems and middleware platforms.
• Experience modernizing or migrating databases and data platforms to the cloud.
• Experience with automation, DevOps practices, and version-controlled deployments.
• Experience with infrastructure as code and CI/CD practices.
• Experience with data virtualization, mirroring, or distributed data processing platforms.
• Experience with SAP HANA or other in-memory databases.
• Experience working with SAP BTP is nice to have but not required.

Preferred Attributes

• Strong analytical and problem-solving skills.
• Ability to balance operational stability with innovation and continuous improvement.
• Demonstrated commitment to continuous learning and evolving data technologies.
• Commitment to teamwork and team collaboration.

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