InPost

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

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SeniorHybryda·WarszawaFull-time

#458626·Dodano miesiąc temu·0

Źródło: InPost

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Tech Stack / Keywords

Databricks Spark SQL Python Airflow KafkadbtGDPRfeature storeLLM

Firma i stanowisko

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.

Wymagania

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.

Obowiązki

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.

Benefity

Opportunity to work in a diverse, international, and cross-functional environment with leading experts.
Work and learn from senior leadership team, gaining exposure to e-commerce and logistics industries.
Growing company with increasing opportunities across multiple European locations.
Range of benefits and investment in training and development.
Participation in the InPost community focusing on sustainability, convenient deliveries, and circular economy.
Excellent working environment and flexible hours.
B2B type of contract offered.

Elastyczne godziny

Dofinansowanie szkoleń

InPost

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