Senior Data Engineer: Fintech Lakehouse & AI Pipelines

InPost; Województwo mazowieckie; On-site; PLN 180,000 - 280,000; Full time

Benefits offered by this job

Flexible hours

International environment

B2B contract

Career development opportunities

Job summary

InPost is building a scale-up with AI integration. You will anchor the data engineering team as the technical authority, owning end-to-end data pipelines for credit origination, underwriting, and reporting.

You’ll design and operate a feature store, ensure point-in-time correctness, and implement agentic loops with guardrails to enable reliable, observable, cost-controlled delivery. You will shape data governance, lineage, and access controls, aligning with GDPR and internal policies, while

Qualifications

• 5+ years in data engineering (pipelines, modelling)
• 3+ years with Spark / Databricks or 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
• Dimensional and event-based data modelling for analytical and ML consumption
• SQL performance tuning and partitioning on large tables
• Python and Spark; orchestration (Airflow / Workflows)
• LS and guardrail design; strong 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

Responsibilities

• Design, build, and operate batch and streaming data pipelines on the lakehouse (Databricks / Spark)
• Build and maintain the feature store for BNPL Origination and MCA Underwriting
• Develop AI-first agentic processes for pipelines with reliable loops, guardrails, and observability
• Model financial and behavioural data with governance for GDPR and internal data governance
• Embed data-quality and observability: freshness, completeness, distribution, schema-drift checks
• Maintain post-production accountability: monitoring, incident response, root-cause analysis
• Work with a feature store with point-in-time data, using Databricks and Spark
• Own agentic harnesses and loop/graph-based agent workflows for feature creation, freshness, and data quality monitoring

Skills

Data engineering

Spark / Databricks

SQL & Python

Agentic tooling

Batch/Streaming pipelines

Dimensional modeling

Partitioning

Airflow

LLM knowledge

Data governance

English (B2+)

Education

University degree in Computer Science, Data Engineering, Mathematics, or related field

Tools

dbt

Airflow

Kafka

Structured Streaming

Job description

InPost is building a scale-up with AI integration. You will anchor the data engineering team as the technical authority, owning end-to-end data pipelines for credit origination, underwriting, and reporting.

You’ll design and operate a feature store, ensure point-in-time correctness, and implement agentic loops with guardrails to enable reliable, observable, cost-controlled delivery. You will shape data governance, lineage, and access controls, aligning with GDPR and internal policies, while