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

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. - 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 hours - We offer B2B type of contract