Lead Data Engineer

<p><strong>Some key info for you about Liberis:&nbsp;</strong></p>

<p>🌱 We were founded in 2007</p>

<p>💰 We have provided over $3bn of funding to small businesses so far</p>

<p>🚀 We have been named in<span class="Apple-converted-space">&nbsp;</span><strong>CNBC &amp; Statista Top 150 UK Fintechs for 2025</strong></p>

<p>🌍 We're a global team, with a dynamic presence in<span class="Apple-converted-space">&nbsp;</span><strong>6</strong><span class="Apple-converted-space">&nbsp;</span>key locations around the world</p>

<p>🧠 We're a thriving community of over<span class="Apple-converted-space">&nbsp;</span><strong>290</strong><span class="Apple-converted-space">&nbsp;</span>innovative minds</p>

<p>👩🏾 🤝 👨🏼 We're a vibrant melting pot, celebrating over<span class="Apple-converted-space">&nbsp;</span><strong>27</strong><span class="Apple-converted-space">&nbsp;</span>nationalities in our team</p>

<p>🏢 Our team brings experience from over<span class="Apple-converted-space">&nbsp;</span><strong>740</strong><span class="Apple-converted-space">&nbsp;</span>previous companies, from startups to global giants</p>

<p>🎯 We have just been named as one of<span class="Apple-converted-space">&nbsp;</span><strong>FinTech’s Finest 50</strong><span class="Apple-converted-space">&nbsp;</span>by Welcome to the Jungle</p>

<p>💪 We’re proud to be an accredited<span class="Apple-converted-space">&nbsp;</span><strong>Real Living Wage</strong><span class="Apple-converted-space">&nbsp;</span>employer, ensuring everyone is paid fairly for the great work they do!</p>

<p>&nbsp;</p>

<p><strong>Our Product &amp; Engineering Team:</strong></p>

<p>Liberis is building the embedded finance platform that lets partners around the world offer innovative funding products to their small business customers. We're a growth-stage fintech with teams in London, Nottingham, Atlanta, Stockholm, Munich and Mumbai, and we’re building a global Product, Data &amp; Engineering team that thrives on autonomy, ownership, and is focused on impact! Our teams solve real-world problems for small businesses, shaping products that unlock opportunity at scale.<span class="Apple-converted-space">&nbsp;</span></p>

<p>Engineering is going through an AI-first transformation, rethinking how teams are structured and how they ship. It's changing what a small team can do! We empower our teams to make decisions, move fast, and take full responsibility for the solutions they deliver. You’ll join a team where curiosity is encouraged and collaboration across Product, Data, Delivery and Engineering is the norm.</p>

<p>&nbsp;</p>

<p><strong>About our Data &amp; Insights Team:</strong></p>

<p>We exist to build the data platforms and analytics that enable every decision at Liberis to be data-informed—and increasingly, to power AI and ML capabilities across the company!</p>

<p>We're building composable, reliable data platforms that scale—from ingesting partner transaction data and event streams, to powering analytics dashboards, to feeding ML models with real-time features. We're also supporting the AI/ML platform team with reliable, low-latency feature pipelines and model serving infrastructure.</p>

<p>We're collaborative, pragmatic, and we value moving fast by fixing the right problems—not over-engineering, but building to last!</p>

<p>&nbsp;</p>

<p><strong>The team is made up of three functions:</strong></p>

<p><strong>Data Platform Engineering:</strong> Building and scaling ELT pipelines, managing data infrastructure on GCP, and creating the foundation for analytics and ML feature stores. You'll be part of a small, high-performing team of platform engineers focused on reliability, scale, and developer velocity.</p>

<p><strong>Analytics Engineering:</strong> Transform raw data into trusted models using DBT and SQL, powering self-serve analytics and business intelligence for stakeholders across the company.</p>

<p><strong>Data &amp; Business Intelligence</strong>: Build dashboards, partner-facing reports, and insights that drive business decisions and revenue outcomes.</p>

<p>&nbsp;</p>

<p><strong>What you'll get to do in the role:</strong></p>

<ul>

<li>Design, build, and maintain resilient data pipelines that ingest data from Azure SQL, SaaS platforms, and event streams into BigQuery.</li>

<li>Write Python code using DLT to define declarative, testable, version-controlled pipelines - no low-code tools, real engineering.</li>

<li>Build and operate ML feature pipelines - low-latency, real-time data streams that feed ML models with accurate, fresh features.</li>

<li>Own the operational health of systems you build - monitoring, alerting, error handling, and incident response. When the data pipeline goes down, merchant credit decisions and ML model predictions suffer.</li>

<li>Collaborate with analytics engineers to understand data needs, validate schema design, and establish data quality standards that both analytics and ML rely on.</li>

<li>Partner with the AI/ML platform team to design feature stores, streaming feature infrastructure, and model serving pipelines that power Liberis' decisioning engine.</li>

<li>Identify and execute optimisation work - improving performance, reliability, and developer velocity without rearchitecting stable systems.</li>

<li>Mentor junior engineers, helping them grow as engineers and supporting their career development.</li>

<li>Participate in technical decisions about platform direction - infrastructure choices, tooling, architecture trade-offs.</li>

<li>Work cross-functionally with product teams, analytics engineers, BI specialists, and the ML platform team to shape data requirements and platform capabilities.</li>

</ul>

<p>&nbsp;</p>

<p><strong>What we think you'll need:</strong></p>

<ul>

<li>Proven experience within data engineering roles -building and operating data pipelines at scale</li>

<li>Hands-on experience building Modern Data Stack architectures - you understand the layers: ingestion, warehouse, transformation, orchestration, reverse ETL. You've worked with tools like DLT/Fivetran/Airbyte (ingestion), BigQuery/Snowflake/Redshift (warehouse), DBT (transformation), Airflow/similar (orchestration).</li>

<li>Strong Python programming - you write clean, testable, maintainable code with solid error handling and logging.</li>

<li>Fluent SQL - you can write complex queries, understand execution plans, and optimize for performance and cost.</li>

<li>Experience with cloud data platforms - you've built data warehouses in BigQuery, Redshift, Snowflake, or similar; you understand distributed processing, partitioning, cost optimization, and data governance.</li>

<li>Experience with infrastructure-as-code tools (Terraform, CloudFormation, Pulumi) or equivalent - you version control infrastructure and deploy it via CI/CD pipelines.</li>

<li>Experience working in fast-moving environments where requirements evolve and you adapt quickly without losing sight of reliability.</li>

<li>Understanding of DevOps principles - you think in terms of observability, resilience, incident response, and operational excellence. You can set up monitoring and alerting that actually matters.</li>

</ul>

<p>&nbsp;</p>

<p><strong>Bonus points if you have:&nbsp;</strong></p>

<ul>

<li>Experience with DLT or similar declarative ELT frameworks; experience with Google Cloud Platform ecosystem (BigQuery, Cloud Run, Pub/Sub, Dataflow); experience with Kafka, Pub/Sub, or event streaming platforms; experience scaling data systems from 0 to 100M+ events/day; experience implementing data quality frameworks (Great Expectations, dbt tests, custom monitoring); background in fintech or high-stakes data reliability environments where data quality directly impacts revenue.</li>

<li>Experience working with distributed, asynchronous teams across timezones; experience in India tech ecosystem or building in resource-constrained environments; experience migrating from legacy data infrastructure (Azure ADF, traditional ETL) to modern cloud-native stacks.</li>

</ul>

<p>&nbsp;</p>

<div class="p-rich_text_section">Career development is really important to us here at Liberis, with progression opportunities for both individual contributors and people managers. You can have a look through our Engineering Career Framework via&nbsp;<a href="https://job-boards.greenhouse.io/liberis/jobs/263e2181723d80a29d3ece5201242561">this link&nbsp;</a></div>

<div class="p-rich_text_section"><br><strong data-stringify-type="bold">Our hybrid approach</strong><br>Working together in person helps us move faster, collaborate better, and build a great Liberis culture. Our hybrid working policy requires team members to be in the office at least 3 days a week. At Liberis, we embrace flexibility as a core part of our culture, while also valuing the importance of the time our teams spend together in the office.<br><br>#LI-FC1</div>

<p>&nbsp;</p>

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