Additionalinformation

Additional Information

• 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

Companydescription

Company Description

We are building a scale-up inside InPost, and AI will flow through it. You would be the anchor hire of the data engineering team: the technical authority whose design decisions every other engineer builds on. You'd work directly with the Head of Data Engineering and the Head of ML, with the InPost executive team backing the build. This is an individual-contributor role with organisation-wide scope. Your influence comes from your technical credibility, not your headcount. Our word is extreme ownership.

Jobdescription

Job Description

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.

Qualifications

• 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