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 in

CNBC & Statista Top 150 UK Fintechs for 2025

🌍 We're a global team, with a dynamic presence in

6

key locations around the world

🧠 We're a thriving community of over

290

innovative minds

👩🏾 🤝 👨🏼 We're a vibrant melting pot, celebrating over

27

nationalities in our team

🏢 Our team brings experience from over

740

previous companies, from startups to global giants

🎯 We have just been named as one of

FinTech's Finest 50

by Welcome to the Jungle

💪 We're proud to be an accredited

Real Living Wage

employer, 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:

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. 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.

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 this link

Liberis is an equal opportunities employer. We welcome applications from all candidates, including individuals with disabilities and provide reasonable adjustments as required.

Our hybrid approach 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. #LI-FC1