Senior Data Analyst

cube; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

CUBE, a global RegTech leader, is seeking a Senior Data Analyst to become the single source of truth for operations performance in Bengaluru. You will turn data from delivery, quality, content sources and APIs into trusted insights that leadership can act on, collaborating with engineering, QA, DevOps and operations leads.

You will design and maintain live dashboards, track delivery metrics, perform variance analysis, and produce executive-ready reports that inform resourcing, capacity planning

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, ETL scripting and JSON/XML parsing.
• SQL: strong hands-on queries including joins, aggregations, window functions.
• BI tooling: Power BI/Tableau/Looker with dashboards shipped to real users.
• Advanced Excel: pivots, Power Query and data models.
• Jira & Confluence: JQL-driven reports and sprint analytics.
• Data modelling: fact/dimension design and KPI definitions.
• Statistical thinking: descriptive statistics and trend analysis.
• Data quality analysis & advisory: profiling datasets and diagnosing root causes.
• Executive-quality communication: turning numbers into actionable reports.
• Ownership mindset: drive reconciliation and continuous improvement.

Responsibilities

• Operational Dashboards: design and maintain live views of throughput, cycle time, turnaround SLAs and blockers.
• Delivery Intelligence: track batch and sprint delivery with weekly burn-downs and forward-looking projections.
• Quality & Defect Analytics: build defect taxonomies and trend reports to identify root causes.
• Source & Adapter Health: monitor coverage and change-detection accuracy across content sources.
• API & Integration Telemetry: report on call volumes, latency, errors, and SLA breaches.
• ROI & Capacity Models: quantify manual effort and automation payback for leadership decisions.
• Executive Reporting Cadence: own daily, weekly and monthly leadership decks."
• Data Quality Stewardship: ensure freshness and accuracy with SLAs across dashboards.

Skills

Python

SQL

BI tooling

Excel advanced

Jira & Confluence

Data modelling

Statistical thinking

Data quality analysis

Executive communication

Ownership mindset

Tools

Azure data stack

API observability tools

Git-based workflows

AI/ML tooling judgements

Job description

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.

Why us?

CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally.

In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead.

We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one.

With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success.

We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.

About the Role

We are looking for a Senior Data Analyst to be the single source of truth for how our operations perform. You will turn data from delivery, quality, content sources and APIs into clear, trusted insights that leadership can act on. You will work closely with engineering, QA, DevOps and operations leads, and your dashboards and reports will directly shape planning, resourcing and investment decisions.

What You'll Own

Operational Dashboards
Design and maintain live views of throughput, cycle time, turnaround SLAs and blocker triage, so team health is visible at a glance.

Delivery Intelligence
Track batch and sprint delivery with weekly burn-downs, variance analysis and forward-looking projections. Flag slippage early, before it becomes a surprise.

Quality & Defect Analytics
Build defect taxonomies, first-pass-yield metrics and trend reports. Correlate quality issues to root causes and route them to the right owners.

Source & Adapter Health
Monitor coverage, failure rates and change-detection accuracy across content sources and adapters. Identify where engineering effort will deliver the biggest lift.

API & Integration Telemetry
Report on call volumes, latency, error rates, auth failures and SLA breaches, and turn observability data into actionable engineering signals.

ROI & Capacity Models
Quantify manual effort, automation payback and throughput uplift. Produce the numbers that inform leadership's resourcing and investment decisions.

Executive Reporting Cadence
Own the daily, weekly and monthly reporting rhythm: scorecards, deep-dives and leadership decks that stakeholders can act on with confidence.

Data Quality Stewardship
Treat every dashboard as a product. Own freshness, accuracy and reconciliation SLAs so numbers are never "roughly right."

What You Bring

Must-have

• 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

• Query language: 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: hands-on with 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 teams concrete, prioritised recommendations to improve quality at the source, not just at the dashboard

• Executive-quality communication: you make numbers mean something and write reports leaders actually read

• Ownership mindset: you chase discrepancies until they reconcile, and "good enough" isn't in your vocabulary

Nice-to-have

• Azure data stack: ADF, Synapse, Log Analytics, Application Insights (or AWS/GCP equivalents)

• Document-processing exposure: XML/XSD, HTML DOM, PDF structure or content-transformation pipelines

• API observability tools: New Relic, Datadog or Application Insights

• Version control discipline: Git-based workflows, code reviews and reproducible analysis

• Automation instinct: you would rather script a report once than build it manually every week

• Advanced BI modelling: DAX, LookML or Power Query M

• AI solutioning judgement: you know when to use an LLM, a classical ML model, a rules engine or plain SQL, and you pick the right tool for the problem instead of forcing GenAI onto everything

What Success Looks Like

• First 30 days: Understand our delivery, quality and API data landscape. Audit existing reports and identify gaps in data trust.

• First 60 days: Ship the core operational and delivery dashboards with agreed KPI definitions and freshness SLAs.

• First 90 days: Run the full daily/weekly/monthly reporting cadence, with automated refreshes and reconciled numbers that leadership relies on.

Interested?

CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.