Lead Data Engineer

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. Our Data & Insights Team builds 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.

Responsibilities

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

Requirements

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.

Nice to 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

Benefits

Accredited Real Living Wage employer; Hybrid working policy (in office at least 3 days a week); Career development and progression opportunities; Equal opportunities employer with reasonable adjustments