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 reportingBuild and maintain the feature store that BNPL Origination and MCA Underwriting consume, guaranteeing point-in-time correctness, freshness SLAs, and reproducibility for every served featureBuild 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-controlledModel financial and behavioural data into governed, documented datasets with clear ownership, lineage, and access controls aligned to GDPR and internal data governanceEmbed data-quality and observability as first-class deliverables: freshness, completeness, distribution, and schema-drift checks with alertingMaintain post-production accountability for owned pipelines — monitoring, incident response, root-cause analysis, and iterative improvementWork 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 lakehouse5+ years using SQL, data modelling, and Python for data engineering2+ years of advanced agentic coding tools and loop engineering — context/tool design, evals, guardrails, iterationBatch and streaming pipeline design on a lakehouse architectureDimensional and event-based data modelling for analytical and ML consumptionSQL performance tuning and partitioning on large tablesPython 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 aboveUniversity degree in Computer Science, Data Engineering, Mathematics, or a related fieldNice to have:
1+ years with feature store / ML data serving1+ years with payments / fintech or lending data1+ years with streaming (Kafka / Structured Streaming)Payment / lending domain data (authorisation, ledger, repayment, delinquency)Credit-risk feature design and scoring-pipeline dataReal-time streaming and CDC patternsdbt or equivalent transformation frameworksCost and performance optimisation on Databricks Opportunity to work in a diverse, international, and cross-functional environment, along with leading experts. You work and learn from a senior leader and their leadership team, expanding your experience and exposure in the e-commerce and logistics industries. InPost is a growing company that offers its people an increasing number of opportunities in several locations in Europe. Fulfilling careers with a range of benefits for people and investing in providing training opportunities for their development. You will feel a part of the InPost community that makes an impact on sustainability, convenient deliveries, and the circular economy every day. Excellent working environment and flexible hoursWe offer B2B type of contract