Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer based in the United States. As a Data Engineer, you will design, build, and operate scalable data platforms that transform massive volumes of structured and unstructured information into reliable, actionable intelligence. You will develop high-performance data pipelines and distributed services across public cloud platforms and on-premises environments. Combining software engineering expertise with infrastructure and platform engineering skills, you will help deliver resilient, efficient, and secure data solutions. You will work with technologies such as Java, Python, Kafka, Kubernetes, and modern database systems to support demanding data processing workloads. Collaborating with customers, integration teams, and solution architects, you will contribute to successful deployments and continuous platform improvement. This remote opportunity is suited to an experienced engineer who enjoys solving complex technical challenges and working with globally distributed teams. Accountabilities:

In this role, you will be responsible for building and maintaining production-grade data infrastructure, optimizing distributed systems, and supporting deployments across cloud and on-premises environments. Your responsibilities will include: Design, develop, and maintain large-scale data ingestion, transformation, and enrichment pipelines.

Build and operate distributed data processing services using Java, Python, Kafka, and related technologies.

Design scalable, resilient, and high-performance data architectures to support complex processing requirements.

Develop and optimize data models for analytics, search, graph, and operational workloads.

Deploy and manage applications in Kubernetes-based environments, using containerization technologies such as Docker.

Work with cloud-native services across AWS, GCP, or other public cloud platforms while supporting hybrid cloud and on-premises deployments.

Collaborate with customers, integration teams, and solution architects to deliver successful technical implementations.

Monitor, troubleshoot, and optimize platform performance, reliability, scalability, and operational stability.

Contribute to infrastructure automation, continuous integration and continuous delivery (CI/CD), and broader platform engineering initiatives.

Travel domestically and internationally as needed to support customer engagements, workshops, and deployments.

Requirements: The ideal candidate will have strong software engineering fundamentals, hands-on experience operating data platforms, and the ability to troubleshoot complex distributed systems. You should be comfortable working across application development, cloud infrastructure, and production operations. Key qualifications include: At least 5 years of experience in Data Engineering, Software Engineering, or Platform Engineering.

Strong programming skills in Java and/or Python.

Hands-on experience with Kubernetes and containerized applications, particularly Docker.

Practical experience with AWS, GCP, or other public cloud platforms.

Experience supporting or operating hybrid cloud and on-premises environments.

A solid understanding of distributed systems and multithreaded applications.

Experience working with SQL and NoSQL databases.

Demonstrated experience building, deploying, and operating production-grade data pipelines.

Strong troubleshooting, operational, and performance optimization skills.

Excellent communication and collaboration skills, including the ability to work effectively with technical teams and customers.

Willingness and ability to travel domestically and internationally when required.

Location requirement:

Must reside in the Eastern or Central U.S. time zone.

The following qualifications are considered advantageous: Experience designing and operating multi-cluster Kubernetes environments.

Experience with data platforms deployed in highly regulated or air-gapped on-premises environments.

Knowledge of networking, security, and cloud infrastructure best practices.

Previous experience working directly with customer-facing engineering teams.

Benefits: Base salary:

$100,000–$120,000 per year, depending on qualifications, experience, location, skills, and performance.

Additional compensation:

Potential eligibility for commission and short- and long-term incentives as part of the overall compensation package.

Health insurance:

Health insurance coverage.

Life and disability insurance:

Coverage to support employee protection and financial security.

Retirement savings:

401(k) plan with employer matching.

Paid time off:

Paid holidays and paid time off (PTO).

Remote work:

Work remotely from the United States, provided you reside in the Eastern or Central time zone.

Technical growth:

Opportunities to work with distributed systems, large-scale data platforms, Kubernetes, and cloud-native technologies across complex deployment environments.

Customer and team collaboration:

Work with experienced colleagues and technical partners on challenging data engineering and platform delivery projects.

How Jobgether works: We use an

AI-powered matching process

to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.