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Senior Data Analyst

CUBE· Technology
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BangaloreFull-timePosted 2mo ago

About the role

Senior Data Analyst to be the single source of truth for how operations perform, turning data from delivery, quality, content sources and APIs into clear, trusted insights for leadership to act on. Collaborates with engineering, QA, DevOps and operations leads; dashboards and reports shape planning, resourcing and investment decisions.

Responsibilities

  • Design and maintain live views of throughput, cycle time, turnaround SLAs and blocker triage
  • Track batch and sprint delivery with weekly burn-downs, variance analysis and forward-looking projections
  • Build defect taxonomies, first-pass-yield metrics and trend reports; correlate quality issues to root causes and route them to owners
  • Monitor coverage, failure rates and change-detection accuracy across content sources and adapters
  • Report on call volumes, latency, error rates, auth failures and SLA breaches; turn observability data into actionable signals
  • Quantify manual effort, automation payback and throughput uplift; inform leadership's resourcing and investment decisions
  • Own the daily, weekly and monthly reporting cadence: scorecards, deep-dives and leadership decks
  • Treat every dashboard as a product; own freshness, accuracy and reconciliation SLAs

Qualifications

  • 3-5 years in a data or analytics role, ideally in a fast-moving product or engineering environment
  • Python: solid working proficiency with pandas, requests, basic ETL scripting and JSON/XML parsing
  • Strong hands-on SQL (or equivalent such as KQL, PromQL, GraphQL or NoSQL dialects) including joins, aggregations, window functions and performance-aware queries
  • BI tooling: Power BI (preferred), Tableau or Looker with dashboards shipped to real users
  • Advanced Excel: pivots, Power Query and structured data models
  • Jira & Confluence: JQL-driven reports, sprint analytics and self-service documentation
  • Data modelling: fact/dimension design, KPI definition and clean semantic layers
  • Statistical thinking: descriptive statistics, cohorting, trend analysis and basic forecasting
  • Data quality analysis & advisory: profiling datasets for completeness, accuracy, consistency and timeliness; diagnosing root causes; and giving concrete, prioritized recommendations
  • Executive-quality communication: ability to make numbers meaningful for leaders
  • Ownership mindset: resolve discrepancies; reject "good enough"
  • Document-processing exposure (nice-to-have)
  • Azure data stack / AWS/GCP equivalents (nice-to-have)
  • API observability tools (nice-to-have)
  • Version control discipline (nice-to-have)
  • Automation instinct (nice-to-have)
  • AI solutioning judgement (nice-to-have)

Skills

Required:PythonSQLBI toolingExcelJira & ConfluenceData modellingStatisticsData quality

Benefits

  • Apply for this Job
Last updated Oct 7, 2026. Interested? Apply on the company site.

About CUBE

Transforming Regulatory Compliance | Reduce Risk & Increase Efficiency with our Compliance Automation Solutions

CUBE offers automated regulatory intelligence solutions & services for global compliance, helping enterprises & midmarket businesses manage regulatory change.

regulatorycomplianceAIregtechregulatory change management
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