Lead Data Engineer

• 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
• Build and operate ML feature pipelines that feed models with accurate, fresh features
• Own the operational health of the systems we build, including monitoring, alerting, error handling, and incident response
• Collaborate with analytics engineers to understand data needs, validate schema design, and establish data quality standards
• Partner with the AI/ML platform team to design feature stores, streaming feature infrastructure, and model serving pipelines
• Identify and execute optimisation work to improve performance, reliability, and developer velocity
• Mentor junior engineers and support their career development
• Participate in technical decisions about platform direction, infrastructure choices, tooling, and 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

Technologies:

• AI
• Airflow
• Redshift
• Azure
• BigQuery
• CI/CD
• Cloud
• DevOps
• ETL
• Embedded
• Fivetran
• Support
• Kafka
• Model Serving
• Pulumi
• Python
• SQL
• Snowflake
• Terraform
• dbt
• Business Intelligence
• GCP

• Proven experience in data engineering roles, building and operating data pipelines at scale
• Hands-on experience with Modern Data Stack architectures, including ingestion, warehouse, transformation, orchestration, and reverse ETL
• Experience with tools such as DLT, Fivetran, or Airbyte for ingestion; BigQuery, Snowflake, or Redshift for warehousing; DBT for transformation; and Airflow or similar for orchestration
• Strong Python programming skills, with the ability to write clean, testable, maintainable code with solid error handling and logging
• Fluent SQL skills, including writing complex queries, understanding execution plans, and optimizing for performance and cost
• Experience with cloud data platforms and distributed processing, including partitioning, cost optimization, and data governance
• Experience with infrastructure-as-code tools such as Terraform, CloudFormation, or Pulumi, and deployment through CI/CD pipelines
• Experience working in fast-moving environments where requirements evolve and reliability remains a priority
• Understanding of DevOps principles, including observability, resilience, incident response, and operational excellence
• Bonus experience with DLT or similar declarative ELT frameworks, Google Cloud Platform, Kafka, Pub/Sub, event streaming platforms, data quality frameworks, fintech, distributed teams, or legacy-to-modern data migration

We are Liberis, founded in 2007 and building the embedded finance platform that helps partners around the world offer innovative funding products to small business customers. We have provided over $3bn of funding so far, are recognised among CNBC and Statistas Top 150 UK Fintechs for 2025, and have also been named one of FinTechs Finest 50 by Welcome to the Jungle. We are a global team of over 290 people across 6 key locations, representing 27+ nationalities and experience from more than 740 previous companies. We are proud to be an accredited Real Living Wage employer. Our Product, Data & Engineering teams operate with autonomy, ownership, and impact, and our Data & Insights team builds the data platforms and analytics that power decision-making and AI/ML capabilities across the business. We offer career development opportunities for both individual contributors and people managers, and our hybrid working policy requires at least 3 days a week in the office.