Full-Time Operations QA Data Analyst

About LendableLendable is on a mission to make consumer finance amazing: faster, cheaper, and friendlier. We're building one of the world’s leading fintech companies and are off to a strong start:

• One of the UK’s newest unicorns with a team of just over 500 people
• Among the fastest-growing tech companies in the UK
• Profitable since 2017
• Backed by top investors including Balderton Capital and Goldman Sachs
• Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot)

So far, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance. We get money into our customers’ hands in minutes instead of days.

We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.

Join us if you want to:

• Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1
• Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo
• Build the best technology in-house, using new data sources, machine learning and AI to make machines do the heavy lifting

About the RoleWe’re looking for a smart, data-driven Operational QA Data Analyst to join our Quality Assurance team and take the lead on analysis and reporting across QA, Training, and Governance. This role is all about using data to understand how we’re performing, spot areas for improvement, and help us deliver better customer outcomes at scale by suggesting and driving improvements to our operational strategy.

You’ll be working closely with a team that reviews all customer interactions—making sure every customer receives excellent service and that we consistently deliver good customer outcomes.

As QA evolves from manual checks to AI-automated processes, you’ll play a key role in shaping a data-led QA framework that supports this transformation. We’re looking for someone who not only understands data, but also knows what great customer outcomes look like—and can bring those two things together through clear, compelling analysis.

Key Responsibilities

• Analyse and investigate data to identify potential sources of poor customer outcomes, collaborate with the QA team to understand the root causes, and partner with product teams to resolve identified issues.
• Support the QA team by identifying trends, risks, and opportunities through data-led monitoring of agent and automated–customer interactions
• Build audit-ready datasets and proactively spot inconsistencies to maintain data accuracy and integrity
• Translate complex datasets into clear, actionable insights and recommendations for senior stakeholders
• Deliver intuitive dashboards and reports that communicate the story behind the data
• Own end-to-end analytical projects—from scoping requirements through to delivery and feedback
• Work with ETL tools (e.g., DBT or similar) to streamline data workflows and ensure scalable, reliable reporting
• Apply statistical thinking to support robust decision-making and QA process improvements
• Contribute to the design and rollout of an AI-automated QA framework through deep analysis and insight generation

Data & Technical Responsibilities

• Write and optimise complex SQL queries to extract, transform, and analyse data
• Ensure data pipelines are accurate, well-documented, and built for scale
• Use DBT and Python to enhance and automate transformation processes and analysis workflows (Desirable)
• Maintain a high standard of data hygiene, including reconciliation, validation, and version control

Cross-Team Collaboration

• Work closely with the QA team to ensure insights directly influence how we monitor and improve customer interactions
• Partner with Product, Risk, Ops Analytics, and other operational teams to align on priorities and improve how data is used across the business
• Confidently present findings and tell the story behind the numbers to both technical and non-technical audiences

What We’re Looking For

• Essential Skills & Experience
• Strong SQL skills, with experience writing and optimising complex queries
• A strong understanding of what drives great customer outcomes—and a desire to improve them
• Proven ability to analyse data and translate it into clear, actionable insights
• Excellent communication skills—you can explain complex analysis in a way that makes sense to different audiences
• Confident in presenting insights and recommendations to senior stakeholders
• Experience working with dashboarding tools (tool agnostic—what matters is your ability to tell a compelling story through data)
• High attention to detail—you notice if something’s slightly off and take pride in getting it right
• Curious, adaptable, and comfortable working in a fast-evolving environment, particularly with AI-enabled QA tools
• Ability to think critically, challenge assumptions, and identify patterns and trends in data
• A solid foundation in statistics and data validation to ensure credible analysis and insight
• Familiarity with data integrity, reconciliation, and documentation best practices
• Desirable Skills
• Proficiency in Python and DBT for data transformation and automation
• Experience with operational or customer service data, especially in regulated environments
• Familiarity with FCA, QA, and risk reporting frameworks
• Experience automating reporting workflows and improving MI processes
• Exposure to financial services or operational risk analytics

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