Data Analytics​/Metrics Engineer

Data Staff, Inc. is seeking a Data Analytics/Metrics Engineer for a long-term contract opportunity with one of our direct clients in Albany, NY

The Data Analytics/Metrics Engineer will use data analytics, observability, scripting, automation, and enterprise application administration to improve operational efficiency, application reliability, and data-driven decision-making. You will strengthen monitoring and analytics capabilities across complex, multi-tier application environments, transform operational data into actionable insights, automate repetitive processes, and improve performance. The ideal candidate combines strong analytical and troubleshooting skills with hands-on technical expertise, automation capabilities, and the ability to collaborate across application development, infrastructure, integration, and business teams.

• Define and implement a roadmap for expanding operational analytics, intelligent monitoring, and automation across enterprise applications and infrastructure.
• Identify opportunities to replace manual analysis and repetitive operational tasks with reusable scripts, automated workflows, and data-driven processes.
• Establish standards for log analytics, data extraction, error analysis, reporting, and operational dashboards.
• Design, develop, and optimize Splunk searches, dashboards, reports, and alerts to monitor application health, system performance, and operational trends.
• Analyze large volumes of application logs, HTTP status codes, API transactions, and infrastructure events to identify patterns, anomalies, and recurring issues.
• Develop advanced SPL queries to aggregate events, normalize dynamic URL paths, identify high-volume failures, and compare daily, weekly, monthly, and quarterly trends.
• Establish operational KPIs and metrics to measure error rates, application reliability, incident trends, and service performance.
• Translate complex log data into clear insights and actionable recommendations for technical teams and management.
• Design and implement automation solutions using Python, Shell/Bash scripting, and Java to streamline data extraction, log processing, system checks, reporting, and operational workflows.
• Develop reusable scripts and utilities to reduce manual effort, improve consistency, and accelerate troubleshooting.
• Automate recurring reports, file analysis, exception identification, and operational data processing.
• Integrate automation with existing monitoring, application support, and deployment processes.
• Establish coding, documentation, scheduling, error-handling, and maintenance standards for reliable automation.
• Define meaningful operational metrics and reporting standards to support proactive issue detection and continuous improvement.
• Identify trends in incidents, system errors, integration failures, and file-transfer activity.
• Present analytics findings, risk indicators, and improvement opportunities to technical leadership and business stakeholders.
• Promote knowledge sharing, technical documentation, automation reuse, and continuous improvement across teams.
• Evaluate the effectiveness of automation and analytics initiatives using measurable outcomes such as time saved, reduced manual effort, faster incident investigation, and improved monitoring coverage.
• Promote an automation-first mindset and encourage continuous improvement.
• Establish measurable objectives for analytics quality, monitoring coverage, and operational efficiency.
• Communicate technical risks and analytical findings clearly to management and stakeholders.
• Prioritize initiatives based on business impact, operational risk, and potential efficiency gains.
• Encourage collaboration, technical development, knowledge sharing, and standardized engineering practices.
• Improve operational visibility, use of Splunk analytics, manual-effort reduction through scripting automation, application issue identification, reporting accuracy, and reliability across enterprise application and integration environments.

• 5+ years of progressive experience in IT operations, data analytics, application support, systems administration, software engineering, or a related technical discipline.
• Strong hands-on experience with Splunk Enterprise/Dynatrace, SPL, log analysis, dashboards, reporting, and operational monitoring.
• Demonstrated scripting or programming experience using Python, Shell/Bash, Java, or comparable automation technologies.
• Experience troubleshooting enterprise applications, analyzing logs,…