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.
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.
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."
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
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?
If you are passionate about leveraging technology to transform regulatory compliance and meet the qualifications outlined above, we invite you to apply. Please submit your resume detailing your relevant experience and interest in CUBE.
CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.