Senior Data Analyst

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.
  

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.