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

Were a global team with a dynamic presence in6key locations around the world

Were a thriving community of over290innovative minds

Were 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 ofFinTechs Finest 50by Welcome to the Jungle

Were proud to be an accreditedReal Living Wageemployer ensuring everyone is paid fairly for the great work they do!

Our Product & Engineering Team:

Liberis is building the embedded finance platform that lets partners around the world offer innovative funding products to their small business customers. Were a growth-stage fintech with teams in London Nottingham Atlanta Stockholm Munich and Mumbai and were building a global Product Data & 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.

Engineering is going through an AI-first transformation rethinking how teams are structured and how they ship. Its 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. Youll join a team where curiosity is encouraged and collaboration across Product Data Delivery and Engineering is the norm.

About our Data & Insights Team:

We exist to build the data platforms and analytics that enable every decision at Liberis to be data-informedand increasingly to power AI and ML capabilities across the company!

Were building composable reliable data platforms that scalefrom ingesting partner transaction data and event streams to powering analytics dashboards to feeding ML models with real-time features. Were also supporting the AI/ML platform team with reliable low-latency feature pipelines and model serving infrastructure.

Were collaborative pragmatic and we value moving fast by fixing the right problemsnot 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. Youll 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 youll 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 youll need:

• 8 years of professional software engineering experience with at least 4-5 years in 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. Youve 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 - youve 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.