Senior Data Platform Engineer - Cloud-Native & Scalable

LexisNexis Risk Solutions; Miamisburg (OH); On-site; USD 95,000 - 159,000; Full time

Benefits offered by this job

Annual incentive bonus

Job summary

LexisNexis Risk Solutions is seeking an experienced Software Engineer to design, develop, and optimize backend data platform services. You will work across distributed systems for data ingestion, orchestration, and downstream delivery, collaborating with product and platform teams.

You will mentor junior engineers, improve reliability, and contribute to CI/CD and testing practices while addressing complex production issues and performance bottlenecks.

Qualifications

• 7+ years of professional Software Engineering experience.
• BS in Engineering, Computer Science, or a related discipline, or equivalent practical experience.
• Experience developing and supporting production backend, distributed, data-intensive, or cloud-native systems strongly preferred.

Responsibilities

• Design, develop, test, deploy, and maintain software supporting large-scale data ingestion and processing workflows.
• Interface with technical personnel, product stakeholders, architects, and team members to understand and finalize complex requirements.
• Translate product, data, and operational requirements into scalable and maintainable software designs.
• Write and review detailed specifications and technical designs for complex system components and cross-system changes.
• Design solutions spanning multiple services and stages of distributed workflows, including orchestration, file movement, preprocessing, ingestion, metadata management, and downstream delivery.
• Develop software capable of efficiently processing large datasets while managing CPU, memory, network, storage, and I/O resources.
• Diagnose and resolve complex software defects and production issues involving application code, databases, storage, Kubernetes workloads, queues, and distributed processing.
• Design for reliability through retries, idempotency, failure recovery, back-pressure, concurrency controls, and graceful handling of partial failures.
• Build and maintain observability through structured logging, metrics, correlation identifiers, dashboards, and actionable alerts.
• Analyze telemetry to identify performance bottlenecks and risks and opportunities for optimization.
• Design and work with complex data models, metadata structures, schemas, and data-processing workflows.
• Develop and optimize interactions with relational databases, data-lake/object storage, messaging systems, and persistence technologies.
• Implement automated testing practices across unit, integration, component, and end-to-end testing.
• Promote development processes, coding standards, code reviews, and engineering best practices.
• Contribute to CI/CD pipelines and automated quality controls for building, testing, packaging, and deploying services.
• Participate in production support, incident investigation, root-cause analysis, and preventative improvements.
• Identify technical debt, scalability constraints, and opportunities to modernize platform components.
• Mentor less‑senior engineers through design discussions, code reviews, troubleshooting, and development guidance.
• Research emerging technologies to improve platform capability, reliability, cost efficiency, or developer productivity.
• Collaborate across engineering, architecture, product, platform, and data teams.
• All other duties as assigned.

Skills

Software Engineering 7+ years

Distributed systems

Cloud-native apps

Kubernetes

SQL & RDBMS

Java

Observability

Education

Bachelor's degree in Engineering/CS

Job description

LexisNexis Risk Solutions is seeking an experienced Software Engineer to design, develop, and optimize backend data platform services. You will work across distributed systems for data ingestion, orchestration, and downstream delivery, collaborating with product and platform teams.

You will mentor junior engineers, improve reliability, and contribute to CI/CD and testing practices while addressing complex production issues and performance bottlenecks.