Background

CloudFactory is changing the way the world works by providing an on-demand, digital workforce for scaling critical business processes in the cloud. We’re also on a mission to create meaningful work for as many people as possible.

Role Summary

As a Senior Data Analyst, you will independently deliver structured analytical interpretation across client workstreams and selected enterprise priorities within Enterprise QSE. You will work closely with stakeholders across Quality, Delivery, Workforce, Finance, Technology, and related functions to apply statistical reasoning, hypothesis-driven analysis, and practical analytical techniques to identify performance risk, explain operational instability, and strengthen decision-making. This role is well suited to someone who can operate confidently in ambiguous environments, move beyond reporting into diagnosis and insight generation, work effectively in spreadsheet-heavy operating contexts, and contribute to stronger analytical discipline across the function.

Responsibilities

Advanced Performance Analysis

• Conduct multi-dimensional analysis across accuracy, throughput, SLA adherence, workforce trends, queue performance, and financial or service-risk indicators.
• Distinguish natural performance variation from meaningful deviation using structured analytical methods.
• Identify likely drivers behind quality dips, adjustment spikes, instability patterns, and workstream deterioration.
• Use segmentation to isolate patterns across worker groups, task types, shifts, workflows, or use cases.
• Provide clear analytical summaries and practical recommendations to Quality and Delivery leadership.

Statistical Analysis & Performance Risk Interpretation

• Apply practical statistical methods to test hypotheses, compare performance segments, and assess whether observed patterns are meaningful.
• Use sound reasoning around variance, distributions, trend interpretation, and sampling when analyzing operational data.
• Support structured intervention analysis where process or workflow changes need to be evaluated.
• Translate statistical findings into practical implications for operational stakeholders.

Sampling Design & Measurement Integrity

• Design and refine sampling approaches for system accuracy measurement, performance validation, and targeted investigations.
• Ensure sampling methods are representative, consistent, and aligned to the analytical purpose.
• Validate accuracy calculations, sample assumptions, and interpretation logic used in reporting and governance.
• Strengthen confidence in accuracy measurement practices across workstreams.

Workstream Performance Signals

• Contribute to the definition and refinement of leading and lagging indicators at workstream level.
• Identify early signs of SLA instability, quality deterioration, throughput stress, rework patterns, or mismatch between internal metrics and client-observed outcomes.
• Improve the usefulness of internal performance measures in reflecting actual service experience.
• Contribute analytical support to at-risk workstream monitoring and related risk reviews.

Reporting Logic & Data Reliability

• Build, validate, and improve dashboards, analytical views, and metric logic across workstreams.
• Use strong SQL and practical Python or R skills to extract, join, validate, and analyze raw operational data.
• Build and maintain advanced spreadsheet-based analytical models, formulas, validation logic, and lightweight scripts where reporting, control, or investigation workflows still rely on Google Sheets or Excel.
• Identify structural data gaps, inconsistent metric definitions, and reporting weaknesses that reduce trust in outputs.
• Partner with relevant teams to improve data quality, reporting consistency, and calculation clarity.

Cross-Functional Analytical Partnership

• Partner with Quality, Delivery, Workforce, Finance, and Technology stakeholders on complex performance-related analysis.
• Translate analytical findings into clear, actionable recommendations for business and operational leaders.
• Support enterprise initiatives such as RCA improvement, workflow redesign, incident analysis, and automation-related performance review through structured analysis.
• Operate effectively in ambiguous environments where data quality, definitions, or system logic may still be evolving.

Capability Support & Analytical Discipline

• Provide review support, practical guidance, and analytical quality checks for junior analysts where needed.
• Help strengthen consistency in documentation, metric interpretation, and reporting logic across the team.
• Apply structured problem-solving methods, including DMAIC where relevant, to improve analytical repeatability and quality.
• Contribute to stronger statistical literacy and analytical consistency within Enterprise QSE through coaching, examples, and review feedback.

Requirements

Must-Have

• 4–5 years of relevant experience in data analytics, business intelligence, performance analytics, or a related analytical role, ideally within operational, service, or production environments.
• Strong SQL skills, including joins, aggregations, trend analysis, and analytical querying.
• Hands-on experience using Python or R for data analysis, investigation, and manipulation.
• Strong spreadsheet capability, including advanced formulas, nested logic, lookup and array functions, cross-sheet modeling, validation controls, and lightweight scripting or automation in Google Sheets or Excel.
• Solid understanding of variance, distributions, sampling, and practical statistical interpretation.
• Ability to structure ambiguous operational problems into hypotheses, analysis paths, and recommendations.
• Ability to interpret leading and lagging indicators in operational performance environments.
• Ability to explain complex analysis clearly to non-technical stakeholders.
• Confidence working independently in evolving, ambiguous, and data-maturing environments.

Nice to Have / Preferred

• Experience designing or improving sampling and accuracy measurement approaches.
• Exposure to intervention analysis, forecasting, or performance risk monitoring.
• Familiarity with BI tools such as Looker, Tableau, or Power BI.
• Experience improving dashboard logic, reporting standards, or metric governance.
• Lean Six Sigma Yellow Belt or Green Belt, or familiarity with structured problem-solving methods.
• Relevant certifications such as Microsoft Data Analyst Associate (PL-300), advanced SQL certification, or statistical analysis coursework/certification.

• Conduct multi-dimensional analysis across accuracy, throughput, SLA adherence, workforce trends, queue performance, and financial or service-risk indicators.
• Distinguish natural performance variation from meaningful deviation using structured analytical methods.
• Identify likely drivers behind quality dips, adjustment spikes, instability patterns, and workstream deterioration.
• Use segmentation to isolate patterns across worker groups, task types, shifts, workflows, or use cases.
• Provide clear analytical summaries and practical recommendations to Quality and Delivery leadership.
• Apply practical statistical methods to test hypotheses, compare performance segments, and assess whether observed patterns are meaningful.
• Use sound reasoning around variance, distributions, trend interpretation, and sampling when analyzing operational data.
• Support structured intervention analysis where process or workflow changes need to be evaluated.
• Translate statistical findings into practical implications for operational stakeholders.
• Design and refine sampling approaches for system accuracy measurement, performance validation, and targeted investigations.
• Ensure sampling methods are representative, consistent, and aligned to the analytical purpose.
• Validate accuracy calculations, sample assumptions, and interpretation logic used in reporting and governance.
• Strengthen confidence in accuracy measurement practices across workstreams.
• Contribute to the definition and refinement of leading and lagging indicators at workstream level.
• Identify early signs of SLA instability, quality deterioration, throughput stress, rework patterns, or mismatch between internal metrics and client-observed outcomes.
• Improve the usefulness of internal performance measures in reflecting actual service experience.
• Contribute analytical support to at-risk workstream monitoring and related risk reviews.
• Build, validate, and improve dashboards, analytical views, and metric logic across workstreams.
• Use strong SQL and practical Python or R skills to extract, join, validate, and analyze raw operational data.
• Build and maintain advanced spreadsheet-based analytical models, formulas, validation logic, and lightweight scripts where reporting, control, or investigation workflows still rely on Google Sheets or Excel.
• Identify structural data gaps, inconsistent metric definitions, and reporting weaknesses that reduce trust in outputs.
• Partner with relevant teams to improve data quality, reporting consistency, and calculation clarity.
• Partner with Quality, Delivery, Workforce, Finance, and Technology stakeholders on complex performance-related analysis.
• Translate analytical findings into clear, actionable recommendations for business and operational leaders.
• Support enterprise initiatives such as RCA improvement, workflow redesign, incident analysis, and automation-related performance review through structured analysis.
• Operate effectively in ambiguous environments where data quality, definitions, or system logic may still be evolving.
• Provide review support, practical guidance, and analytical quality checks for junior analysts where needed.
• Help strengthen consistency in documentation, metric interpretation, and reporting logic across the team.
• Apply structured problem-solving methods, including DMAIC where relevant, to improve analytical repeatability and quality.
• Contribute to stronger statistical literacy and analytical consistency within Enterprise QSE through coaching, examples, and review feedback.

• SQL
• Python
• R
• Spreadsheet proficiency (Google Sheets, Excel)
• Statistical analysis
• Data interpretation
• Problem-solving
• Communication
• Independent work
• Ambiguity management

• 4–5 years of relevant experience in data analytics, business intelligence, performance analytics, or a related analytical role, ideally within operational, service, or production environments.
• Strong SQL skills, including joins, aggregations, trend analysis, and analytical querying.
• Hands-on experience using Python or R for data analysis, investigation, and manipulation.
• Strong spreadsheet capability, including advanced formulas, nested logic, lookup and array functions, cross-sheet modeling, validation controls, and lightweight scripting or automation in Google Sheets or Excel.
• Solid understanding of variance, distributions, sampling, and practical statistical interpretation.
• Ability to structure ambiguous operational problems into hypotheses, analysis paths, and recommendations.
• Ability to interpret leading and lagging indicators in operational performance environments.
• Ability to explain complex analysis clearly to non-technical stakeholders.
• Confidence working independently in evolving, ambiguous, and data-maturing environments.
• Experience designing or improving sampling and accuracy measurement approaches.
• Exposure to intervention analysis, forecasting, or performance risk monitoring.
• Familiarity with BI tools such as Looker, Tableau, or Power BI.
• Experience improving dashboard logic, reporting standards, or metric governance.
• Lean Six Sigma Yellow Belt or Green Belt, or familiarity with structured problem-solving methods.
• Relevant certifications such as Microsoft Data Analyst Associate (PL-300), advanced SQL certification, or statistical analysis coursework/certification.