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

We are building a scale-up inside InPost focused on data engineering with AI integration. The role is supported by the Head of Data Engineering, the Head of ML, and the InPost executive team.

• 5+ years in data engineering including pipelines and modelling.
• 3+ years experience with Spark / Databricks or equivalent lakehouse.
• 5+ years using SQL, data modelling, and Python for data engineering.
• 2+ years experience with advanced agentic coding tools and loop engineering: context/tool design, evals, guardrails.
• Experience designing batch and streaming pipelines on lakehouse architecture.
• Expertise in dimensional and event-based data modelling for analytical and ML consumption.
• SQL performance tuning and partitioning on large datasets.
• Experience with orchestration tools e.g. Airflow / Workflows.
• Knowledge of data lineage, cataloguing, and governance including GDPR.
• English proficiency at B2 or above.
• University degree in Computer Science, Data Engineering, Mathematics, or related field.

Nice to have:

• 1+ year with feature store / ML data serving.
• 1+ year experience with payments, fintech, or lending data.
• 1+ year experience with streaming technologies such as Kafka / Structured Streaming.
• Experience with payment/lending domain data (authorization, ledger, repayment, delinquency).
• Credit-risk feature design and scoring pipeline data.
• Real-time streaming and CDC pattern experience.
• Experience with dbt or equivalent transformation frameworks.
• Cost and performance optimization on Databricks.

• Design, build, and operate batch and streaming data pipelines on the lakehouse platform (Databricks / Spark).
• Build and maintain the feature store consumed by BNPL Origination and MCA Underwriting.
• Design and operate agentic processes and agent loops including context, tools, evals, and guardrails.
• Model financial and behavioural data into governed, documented datasets with ownership, lineage, and access controls.
• Embed data quality and observability checks including freshness, completeness, distribution, and schema-drift.
• Maintain post-production accountability including monitoring, incident response, root-cause analysis, and iterative improvement.
• Own agentic harnesses and loop- and graph-based agent workflows for feature creation, freshness, and data-quality monitoring.