Job Overview
Location
London
Job Type
Full-time
Category
Data Science
Date Posted
July 4, 2026
Full Job Description
đź“‹ Description
- • Build and improve dbt models and ELT transformations that power credit decisions, loan origination, portfolio analysis, and investor reporting for Lendable’s US Cards team.
- • Collaborate closely with data analysts, product teams, backend engineers, and business stakeholders to ensure data is accurately modelled, accessible, and used effectively across the organization.
- • Contribute to the analytical foundation of the US Cards business by designing scalable, reliable, and maintainable data models that support rapid insight generation.
- • Identify and implement efficiency gains in the data transformation pipeline to reduce costs, improve reliability, and enhance performance as the business scales.
- • Work within a modern data stack including Snowflake, dbt, Fivetran, and Claude to transform raw data into trusted analytical datasets.
- • Act as a bridge between technical and non-technical teams to translate business needs into data requirements and ensure alignment on data definitions and usage.
- • Support the growth of data infrastructure by adapting models and processes to accommodate increasing data volume, complexity, and stakeholder demand.
- • Use AI tools to accelerate development, improve code quality, and automate repetitive tasks within the analytics engineering workflow.
- • Continuously deepen SQL expertise and data modelling knowledge under the guidance of senior engineers, with opportunities to learn best practices in analytical data design.
- • Participate in peer reviews, documentation efforts, and knowledge sharing to elevate the quality and consistency of the team’s data products.
- • Help establish and promote shared patterns and standards for data transformation that improve usability and reduce duplication across analytical teams.
- • Contribute to the evolution of Lendable’s data platform by providing feedback on tooling, processes, and infrastructure to support long-term scalability.
- • Engage in cross-functional initiatives to ensure data quality and governance are embedded into product development and business decision-making cycles.
- • Maintain awareness of industry trends in fintech data engineering and apply relevant innovations to improve Lendable’s analytical capabilities.
🎯 Requirements
- • Solid SQL skills and a willingness to continuously improve them
- • Some hands-on experience with dbt or ELT pipelines
- • Collaborative working style with clear communication across technical and non-technical stakeholders
- • Growing understanding of data modelling principles for reliability and usability
- • Comfort using AI tools to enhance productivity and output quality
🏖️ Benefits
- • Flexible working: hybrid roles require three days in-office weekly; fully remote roles include regular in-person socials and off-sites
- • Private health cover for physical and mental wellbeing
- • Retirement and savings plans to support long-term financial wellbeing
- • Complimentary lunches and fully stocked kitchen on in-office days at select locations
- • Cycle-to-work and electric vehicle salary sacrifice schemes available in select locations
- • Employee referral programme with competitive bonuses for successful referrals
Skills & Technologies
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About Lendable Ltd
Lendable is a UK-based fintech that operates an online consumer-lending platform using open-banking data and machine-learning underwriting to offer unsecured personal loans and car finance. Founded in 2014 and headquartered in London, it funds loans through an institutional peer-to-peer model, matching investors with borrowers seeking fast, fixed-rate credit. The company automates identity, affordability and risk assessment, providing near-instant decisions and same-day payouts while giving investors access to diversified consumer credit returns. Regulated by the Financial Conduct Authority, Lendable has originated more than ÂŁ2 billion in loans without maintaining physical branches, focusing on transparent pricing and mobile-first customer experience.
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