Key Responsibilities and Required Skills for Analyst - Operations

🎯 Role Definition

The Analyst - Operations (Operations Analyst) is responsible for collecting, analyzing, and transforming operational data into actionable insights that improve efficiency, reduce cost, and support strategic decision-making. This role blends quantitative analysis, process improvement, and stakeholder partnership to drive measurable improvements across supply chain, customer operations, finance operations, or general business operations. The Operations Analyst produces operational reporting, monitors KPIs, designs process improvements, supports automation initiatives, and ensures data integrity across multiple systems.

Key keywords: operations analyst, operational reporting, process optimization, KPI tracking, automation, SQL, Tableau, Excel, Lean Six Sigma, stakeholder management.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Business Analyst (Operations) or Data Analyst Intern
Customer Support / Operations Coordinator with strong analytical skills
Finance or Supply Chain Analyst with first-line operational experience

Advancement To:

Senior Operations Analyst
Operations Manager or Program Manager (Operations Improvement)
Process Improvement Lead / Continuous Improvement Manager

Lateral Moves:

Business Intelligence Analyst
Product Operations Analyst
Project Manager (Operational Projects)

Core Responsibilities

Primary Functions

Collect, validate, and transform large operational datasets from multiple sources (ERP, CRM, WMS, custom logs) to create consistent, reusable operational reports and dashboards that track KPIs such as throughput, cycle time, SLA adherence, cost per transaction, and utilization.
Design, build and maintain automated dashboards and visualizations (Power BI, Tableau, Looker) that provide real-time operational visibility for stakeholders across operations, finance, and executive teams; ensure dashboards are accurate, performant, and user-friendly.
Write complex SQL queries and stored procedures to extract, join, aggregate, and clean transactional data; optimize queries for performance and scalability to support daily operational reporting and ad-hoc analysis.
Lead root-cause analysis for operational incidents and recurring performance gaps, translating findings into prioritized corrective actions and process changes with measurable outcomes.
Partner with cross-functional business owners (operations, supply chain, finance, customer success, engineering) to define requirements, translate business needs into analytic specifications, and deliver data-driven solutions that reduce cycle times or costs.
Monitor and report on daily/weekly/monthly operational KPIs, creating exception-based alerts and automated notifications to ensure timely intervention for SLA breaches and critical deviations.
Develop and document Standard Operating Procedures (SOPs), data dictionaries, and runbooks for analytics processes to ensure reproducibility and knowledge transfer across the operations team.
Drive continuous process improvement initiatives (Lean, Six Sigma, Kaizen) by mapping current-state workflows, quantifying improvement opportunity, and implementing pilots that increase efficiency or quality.
Support operational capacity planning and forecasting by building models that predict workload, staffing needs, and resource utilization using historical data and scenario analysis.
Execute A/B tests or controlled experiments for operational process changes, analyze experiment results, and present statistically sound recommendations for scaling successful pilots.
Collaborate with engineering and data teams to define ETL requirements, data quality checks, and schema changes to support robust reporting and analytics pipelines.
Build and maintain automation scripts (VBA, Python, or R) to eliminate manual, repetitive tasks, increase data reliability, and accelerate delivery of operational reports.
Conduct cross-functional workshops to gather requirements, align on KPIs, and socialize analytic outputs; translate stakeholder feedback into iterative dashboard and reporting improvements.
Prepare and present weekly/monthly operational performance reviews and executive summaries incorporating trend analysis, root cause insights, and recommended action plans for leadership.
Support budgets and financial reconciliation by analyzing transaction-level cost drivers, identifying variances, and recommending operational cost controls or efficiency opportunities.
Manage ad-hoc operational analyses and tactical investigations (e.g., order backlog drivers, defect rate trends, cost-to-serve analysis) with the ability to deliver clear, prioritized recommendations under tight timelines.
Implement and maintain data quality frameworks including validation checks, anomaly detection rules, and data lineage documentation to ensure trust in operational metrics.
Act as liaison during incident management and post-mortem processes: compile timeline of operational events, quantify impact, and recommend permanent fixes to prevent recurrence.
Evaluate vendors and third-party solutions for operations tooling; run POCs, gather metrics, and write business cases to support automation or tool adoption initiatives.
Train operations teams on new dashboards, tools, and standardized processes; create user guides and provide hands-on training to ensure adoption and consistent use.
Support compliance and audit requests by preparing accurate operational evidence, data extracts, and documented procedures that demonstrate controls and process adherence.
Continuously scan for opportunities to apply machine learning or advanced analytics (forecasting, anomaly detection) to high-impact operational problems and collaborate with data science teams to prototype solutions.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Maintain and update operational SOPs and runbooks as processes evolve to ensure consistent execution across shifts and regions.
Facilitate cross-team communication during major operational change rollouts and support post-implementation monitoring.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL (window functions, CTEs, performance tuning) for extracting and manipulating transactional and time-series data.
Strong Excel skills (pivot tables, advanced formulas, VBA/macros) for quick analysis and prototyping of operational models.
Experience building dashboards and visualizations in Power BI, Tableau, or Looker with a strong sense of UX and storytelling.
Familiarity with Python or R for scripting, data transformation, automation, and statistical analysis.
Knowledge of ETL processes, data warehousing concepts, and working with cloud data platforms (Snowflake, Redshift, BigQuery).
Hands-on experience with ERP and operational systems (SAP, Oracle, Netsuite), CRMs (Salesforce), or WMS/TMS depending on domain.
Working understanding of process improvement frameworks (Lean, Six Sigma, Kaizen) and ability to apply DMAIC or PDCA methodologies.
Basic statistical and forecasting techniques (time series, regression, hypothesis testing) to support capacity planning and experiment analysis.
Experience with ticketing and collaboration tools (JIRA, ServiceNow, Confluence) to manage operational requests and documentation.
Familiarity with automation tools and scripting for RPA or workflow automation (UIPath, Power Automate) is a plus.
Data quality and validation techniques, including anomaly detection, monitoring, and data lineage documentation.
Experience producing executive-level presentations and one-page scorecards that summarize operational performance and proposed actions.

Soft Skills

Strong analytical thinking with the ability to translate complex data into clear, actionable recommendations.
Excellent verbal and written communication skills for presenting findings to both technical and non-technical stakeholders.
Proactive problem-solving orientation and a bias for action; able to prioritize high-impact tasks in a fast-paced environment.
Cross-functional collaboration and stakeholder management skills; builds credibility with operations, finance, and engineering teams.
Attention to detail and commitment to data accuracy and reproducibility.
Adaptability and resilience when managing competing priorities and operational escalations.
Time management and organization skills to deliver recurring reporting while handling ad-hoc investigations.
Facilitation skills for running workshops, requirement-gathering sessions, and process mapping exercises.
Customer-focused mindset with an emphasis on improving internal and external service levels.
Initiative to learn new tools and continuous improvement mindset to enhance processes over time.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Business, Finance, Economics, Operations Management, Industrial Engineering, Data Analytics, Computer Science, or related field.

Preferred Education:

Master’s degree in Business Analytics, Operations Research, MBA, or a technical field.
Professional certifications such as Lean Six Sigma (Green/Black Belt), CBIP, or relevant analytics certificates (Tableau, Power BI).

Relevant Fields of Study:

Business Administration
Operations Management
Industrial Engineering
Data Analytics / Data Science
Finance / Accounting
Computer Science

Experience Requirements

Typical Experience Range: 2–5 years in operations analysis, business analysis, supply chain analytics, or process improvement roles.

Preferred: 3–7+ years of experience with demonstrated impact on operational KPIs, experience with automation and reporting tools, and exposure to cross-functional project leadership. Experience in industry-specific operations (e.g., logistics, retail, SaaS customer operations, manufacturing) is a strong plus.