Data Reliability Engineer

Job summary

Monitor and troubleshoot data pipelines, storage systems, and analytics platforms; investigate failures and anomalies, perform root cause analysis, and implement practices to prevent recurrence. Collaborate with engineering and operations teams to improve resilient architectures, incident response, observability, automation, documentation, and reliability metrics, including through on-call support.

Job details

Company Description Rockland Tech Hub is a technology solutions provider that helps startups, growing businesses, and enterprises modernize systems, streamline operations, and build secure, scalable digital platforms. The company delivers reliable IT services, intelligent automation, custom software development, web and mobile applications, e-commerce solutions, software testing, branding support, and IT staffing services. Teams at Rockland Tech Hub combine deep technical expertise with practical business insight to create solutions that improve efficiency, strengthen performance, and support sustainable growth. Whether organizations need responsive websites, custom business applications, workflow automation, quality assurance, or skilled IT professionals, Rockland Tech Hub offers dependable, future-ready solutions aligned with client goals. Role Description The Data Reliability Engineer is a full-time remote role focused on ensuring the stability, reliability, and performance of data pipelines, storage systems, and analytics platforms. This role involves monitoring data systems, identifying and investigating failures or anomalies, and implementing reliability-centered maintenance practices to prevent recurrence. The engineer will collaborate with data engineers, software developers, and operations teams to design resilient architectures, optimize data flow, and improve incident response processes. Day-to-day activities include troubleshooting reliability issues, conducting root cause and failure analysis, creating documentation and runbooks, and contributing to automation and tooling that enhance observability and system reliability. The role also includes participating in on-call rotations, supporting continuous improvement initiatives, and helping define reliability metrics and best practices across the organization. Qualifications Strong foundation in Reliability Engineering, with experience designing and maintaining resilient data systems and infrastructure. Advanced Analytical Skills for interpreting logs, metrics, and performance data, and for driving data-informed reliability improvements. Hands-on experience with Failure Analysis and root cause investigation in complex, distributed or data-intensive environments. Knowledge of Reliability Centered Maintenance principles and their application to data platforms, pipelines, and services. Proven Troubleshooting skills for diagnosing and resolving incidents across databases, ETL/ELT processes, and cloud services. Experience with modern data stacks (e.g., SQL/NoSQL databases, data warehouses/lakes, streaming frameworks, ETL tools) and cloud platforms (AWS, Azure, or GCP). Familiarity with observability tools (monitoring, logging, tracing) and scripting or programming languages (such as Python, Bash, or similar). Strong communication skills and ability to collaborate with cross-functional teams in a remote environment. Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field, or equivalent practical experience. Experience with automation, infrastructure-as-code, or SRE practices is a plus.

What you’ll do

Monitor and troubleshoot data pipelines, storage systems, and analytics platforms; investigate failures and anomalies, perform root cause analysis, and implement practices to prevent recurrence. Collaborate with engineering and operations teams to improve resilient architectures, incident response, observability, automation, documentation, and reliability metrics, including through on-call support.

Requirements

Requires a foundation in reliability engineering and experience with resilient data systems, failure analysis, troubleshooting, and reliability-centered maintenance. Candidates should have experience with modern data stacks and cloud platforms, familiarity with observability tools and scripting or programming, strong collaboration skills, and a bachelor’s degree in a related field or equivalent practical experience.

Listed skills

• Root Cause Analysis · Preferred
• Troubleshooting · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Reliability Engineering
• Data Pipeline Reliability
• Analytical Skills
• Failure Analysis
• Root Cause Analysis
• Reliability-Centered Maintenance
• Troubleshooting
• SQL and NoSQL Databases
• Data Warehouses and Lakes
• Streaming Frameworks
• ETL/ELT
• Cloud Platforms
• Observability
• Python
• Bash
• Infrastructure as Code

Job areas

• Technology
• Data & Analytics
• Software
• Engineering

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