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Open nowPosted 6 hours ago

Staff Engineer, Replication

DDN100 open roles

Where
Remote - North Carolina
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Your applicationOpen nowStaff Engineer, ReplicationDDN · Remote - North Carolina

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The clock on this job

Early applications get read.

7.7% of postings close within 7 days. Measured by our own scanner across the market. DDN postings stay open a median of 21 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 6 hours ago

DDN median: 21 days open

The posting

DDN is seeking a Staff Replication Development Engineer to lead the design and development of the replication engine for the Infinia AI Data Platform. This role focuses on building enterprise-grade asynchronous replication capabilities that enable reliable and secure disaster recovery for large-scale data systems.

You will work on developing high-performance replication pipelines, efficient data synchronization mechanisms, and secure data transfer systems. This role requires deep expertise in distributed systems and strong technical leadership to deliver a scalable and resilient replication foundation.

KEY RESPONSIBILITIES

- Design and develop multi-threaded asynchronous replication systems with parallel streaming capabilities

- Build object-level delta replication with checkpointing and resume functionality

- Develop replication engines supporting bucket/share-level replication controls

- Implement secure data transfer mechanisms using TLS 1.3 with mutual authentication

- Ensure end-to-end data integrity through checksum validation and verification pipelines

- Design and implement manual failover workflows for disaster recovery scenarios

- Build and maintain REST APIs for replication configuration, control, and automation

- Develop metadata tracking and change detection systems to enable efficient replication

- Implement RPO visibility, alerting, and operational insights for replication status

- Contribute to monitoring dashboards focused on replication health and performance

- Ensure systems are designed for high availability, fault tolerance, and scalability

- Partner with QA teams to drive performance, resiliency, and scale validation

- Collaborate with backend, security, and platform teams to deliver end-to-end replication workflows

- Participate in debugging, production issue resolution, and continuous improvement of replication reliability

- Provide technical leadership, architectural guidance, and mentorship to the engineering team

REQUIRED QUALIFICATIONS

- 8+ years of experience in distributed systems, storage systems, or backend software engineering

- Strong programming skills in one or more languages: C++, Go, Java, or Rust

- Experience designing and building data replication systems, data pipelines, or distributed data services

- Deep understanding of distributed systems concepts (consistency, availability, scalability, fault tolerance)

- Strong expertise in multi-threading, concurrency, and parallel processing

- Knowledge of networking protocols and secure communication (TCP/IP, HTTP/HTTPS, TLS)

- Experience implementing data integrity mechanisms (checksums, validation, consistency checks)

- Experience designing and building REST APIs and service-based architectures

- Familiarity with checkpointing, failure recovery, and retry mechanisms in distributed systems

- Basic understanding of observability concepts (metrics, logging, alerting)

- Strong debugging, problem-solving, and system design skills

PREFERRED QUALIFICATIONS

- Experience with asynchronous replication, disaster recovery (DR), or backup systems

- Familiarity with object storage or large-scale data storage systems

- Knowledge of delta encoding, change data capture, or incremental data synchronization techniques

- Experience building high-throughput, low-latency data movement systems

- Exposure to security practices including mutual TLS, encryption, and authentication

- Experience working on enterprise-scale data platforms or storage products

- Familiarity with performance optimization and large-scale system tuning

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