Supply Chain Operations Analyst

ID Logistics US•Remote,

16h•$71,000 - $96,000•Remote

About The Position

The Operations Analyst will support Operations, Engineering, Finance, Business Intelligence, and customer leadership teams through advanced research, operational analysis, dashboard development, reporting, automation, systems integration, and continuous improvement initiatives. This role is responsible for analyzing large and complex data sets, identifying trends and performance drivers, developing actionable insights, and translating business needs into scalable reporting and process improvement solutions. This position is highly analytical and research-driven, requiring the ability to synthesize complex operational, financial, engineering, and business performance data into clear reports, dashboards, visualizations, and recommendations that improve visibility, support strategic decision-making, and drive operational performance across the organization.

Requirements

Bachelor’s degree required in Business, Economics, Statistics, Industrial Engineering, Supply Chain, Operations Management, Business Analytics, Information Systems, Data Analytics, or a related field.
1+ years of experience in business research, data analysis, analytics, operations, engineering, continuous improvement, supply chain, or a related analytical role.
Strong Microsoft Excel skills, including experience using Excel for data analysis.
Experience working with SQL for data extraction and analysis.
Experience with data visualization tools such as Power BI, Tableau, Qlik, Looker, or similar platforms.
Strong research and analytical skills, including the ability to interpret complex data trends, statistical findings, key metrics, and performance drivers.
Strong problem-solving and organizational skills with the ability to manage multiple projects, priorities, and competing deadlines.
Excellent verbal and written communication skills with the ability to present findings and recommendations to technical and non-technical audiences.
Ability to work independently in a fast-paced environment with competing priorities.
Flexibility with work schedule and hours when required.

Nice To Haves

Industrial Engineering background.
Internship or professional experience in logistics, warehousing, supply chain, manufacturing, or 3PL operations.
Experience with Alteryx, Python, Power Automate, n8n, or similar automation tools.
Experience building dashboards and reporting solutions.
Experience supporting system integrations, APIs, ETLs, SFTP, or other data exchange processes.

Responsibilities

Lead research and analysis efforts using large operational, engineering, financial, and business data sets to identify trends, patterns, opportunities, and performance drivers.
Analyze business performance across different operational areas and provide data-driven recommendations to improve efficiency, productivity, and overall performance.
Conduct root cause analysis and translate findings into actionable recommendations for leadership teams.
Monitor key business, financial, productivity, KPI, SLA, and contractual performance indicators to identify challenges, risks, trends, and opportunities for improvement.
Provide insights related to budgetary forecasts, financial performance, operational efficiencies, and future business risks or opportunities.
Design, develop, and maintain research reports, dashboards, and visualizations using tools such as Power BI, Tableau, Excel, Qlik, Looker, or similar platforms.
Partner with business stakeholders to define reporting requirements, success metrics, key research questions, and customer-specific reporting needs.
Improve visibility into operational, maintenance, engineering, financial, and business performance through clear, scalable reporting solutions.
Ensure all research outputs, dashboards, reports, and visualizations are aligned with business objectives and presented clearly to internal teams, leadership, and customers.
Develop and maintain SQL queries to support data extraction, validation, reconciliation, and reporting.
Investigate data quality issues and support reconciliation efforts to improve reporting accuracy and consistency.
Collaborate with Business Intelligence teams to ensure operational reporting, dashboards, and analytical outputs are accurate, consistent, and reliable.
Support API, SFTP, ETL, data exchange, and system integration initiatives with internal and external partners.
Assist with testing, validation, deployment, and improvement of data exchange processes and system integrations.
Develop or support automation solutions using tools such as Python, Alteryx, Power Automate, n8n, or similar platforms.
Identify opportunities to eliminate manual processes through automation, workflow improvements, and reporting enhancements.
Work with IT, Internal Systems, and Business Intelligence teams to explore automated data collection, reporting capabilities, and new tools or methodologies that improve research accuracy and efficiency.
Support AI-driven process improvements and gain exposure to artificial intelligence, machine learning, and generative AI technologies as applicable.
Partner with Operations Managers, General Managers, Engineering leadership, Finance, Business Intelligence, and customer teams to understand business challenges and analytical needs.
Collaborate with leadership teams to define research questions, deliver insights, and tailor reports to business and customer needs.
Serve as a trusted resource for operational reporting, performance analysis, and research-based recommendations.
Present analytical findings, research results, and recommendations to both technical and non-technical audiences.
Respond to ad hoc research, reporting, and analytical requests from internal teams, customers, Operations, Engineering, and Finance.
Support continuous improvement initiatives by analyzing operational KPIs, labor productivity, performance trends, and business challenges.
Assist senior leadership with strategic decision-making by conducting research to forecast future trends, risks, and opportunities.
Maintain a focus on research innovation, operational improvement, automation, and scalable analytics practices.