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