Lead Data Engineer

Some key info for you about Liberis:

🌱 We were founded in 2007

💰 We have provided over $3bn of funding to small businesses so far

🚀 We have been named inCNBC & Statista Top 150 UK Fintechs for 2025

🌍 We're a global team, with a dynamic presence in6key locations around the world

🧠 We're a thriving community of over290innovative minds

👩🏾 🤝 👨🏼 We're a vibrant melting pot, celebrating over27nationalities in our team

🏢 Our team brings experience from over740previous companies, from startups to global giants

🎯 We have just been named as one ofFinTech’s Finest 50by Welcome to the Jungle

💪 We’re proud to be an accreditedReal Living Wageemployer, ensuring everyone is paid fairly for the great work they do!

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 at a point where the platform challenges are genuinely interesting: we're not just adding features, we're building financial infrastructure that didn't exist a year ago. 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!

About our Data & Insights Team:

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!

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.

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

The team is made up of three functions:

Data Platform Engineering: 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.

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

Data & Business Intelligence: Build dashboards, partner-facing reports, and insights that drive business decisions and revenue outcomes.

What you'll get to do in the role:

• Design, build, and maintain resilient data pipelines that ingest data from Azure SQL, SaaS platforms, and event streams into BigQuery.
• Write Python code using DLT to define declarative, testable, version-controlled pipelines - no low-code tools, real engineering.
• Build and operate ML feature pipelines - low-latency, real-time data streams that feed ML models with accurate, fresh features.
• 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.
• Collaborate with analytics engineers to understand data needs, validate schema design, and establish data quality standards that both analytics and ML rely on.
• Partner with the AI/ML platform team to design feature stores, streaming feature infrastructure, and model serving pipelines that power Liberis' decisioning engine.
• Identify and execute optimisation work - improving performance, reliability, and developer velocity without rearchitecting stable systems.
• Mentor junior engineers, helping them grow as engineers and supporting their career development.
• Participate in technical decisions about platform direction - infrastructure choices, tooling, architecture trade-offs.
• Work cross-functionally with product teams, analytics engineers, BI specialists, and the ML platform team to shape data requirements and platform capabilities.

What we think you'll need:

• Proven experience within data engineering roles -building and operating data pipelines at scale
• 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).
• Strong Python programming - you write clean, testable, maintainable code with solid error handling and logging.
• Fluent SQL - you can write complex queries, understand execution plans, and optimize for performance and cost.
• 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.
• Experience with infrastructure-as-code tools (Terraform, CloudFormation, Pulumi) or equivalent - you version control infrastructure and deploy it via CI/CD pipelines.
• Experience working in fast-moving environments where requirements evolve and you adapt quickly without losing sight of reliability.
• 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.

Bonus points if you have:

• 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.
• 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.