Senior Data Engineer - Fintech Engineering (m/f/n)

About this role

Your role: Design, build, and operate batch and streaming data pipelines on the lakehouse (Databricks / Spark) that ingest, validate, and model data for credit origination, underwriting, and portfolio reporting Build and maintain the feature store that BNPL Origination and MCA Underwriting consume, guaranteeing point-in-time correctness, freshness SLAs, and reproducibility for every served feature Build and operate AI-first / agentic processes for the team's pipelines — designing the agent loop (context, tools, evals, guardrails) so agentic delivery is reliable, observable, and cost-controlled Model financial and behavioural data into governed, documented datasets with clear ownership, lineage, and access controls aligned to GDPR and internal data governance Embed data-quality and observability as first-class deliverables: freshness, completeness, distribution, and schema-drift checks with alerting Maintain post-production accountability for owned pipelines — monitoring, incident response, root-cause analysis, and iterative improvement Work with a feature store with point-in-time-correct data, using Databricks and Spark, with tracing for pipeline observability.

You will own agentic harnesses and loop- and graph-based agent workflows around the feature store. You design the agent loop (context, tools, state, evals, guardrails) for feature creation, freshness, and data-quality monitoring. 5+ years in data engineering (pipelines, modelling) 3+ years with Spark / Databricks or an equivalent lakehouse 5+ years using SQL, data modelling, and Python for data engineering 2+ years of advanced agentic coding tools and loop engineering — context/tool design, evals, guardrails, iteration Batch and streaming pipeline design on a lakehouse architecture Dimensional and event-based data modelling for analytical and ML consumption SQL performance tuning and partitioning on large tables Python and Spark; orchestration (e.g.

Airflow / Workflows) Quriosity to do Advanced agentic tooling and loop and graph engineering. Eval and guardrail design. Solid LLM working knowledge. Data lineage, cataloguing, and governance thinking (GDPR-aware) English proficiency at B2 or above University degree in Computer Science, Data Engineering, Mathematics, or a related field Nice to have: 1+ years with feature store / ML data serving 1+ years with payments / fintech or lending data 1+ years with streaming (Kafka / Structured Streaming) Payment / lending domain data (authorisation, ledger, repayment, delinquency) Credit-risk feature design and scoring-pipeline data Real-time streaming and CDC patterns dbt or equivalent transformation frameworks Cost and performance optimisation on Databricks Opportunity to work in a diverse , international , and cross-functional environment, along with leading experts .