Lead Data Engineer, Liberis

• 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

Desirable

• 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
• 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