28 days ago
Warsaw, PolandSenior
Responsibilities
- Design, build, and operate batch and streaming data pipelines on a Databricks and Spark lakehouse.
- Build and maintain a point-in-time-correct feature store consumed by BNPL Origination and MCA Underwriting.
- Design and operate AI-first agentic processes, including agent context, tools, state, evaluation, and guardrails.
- Model financial and behavioral data into governed, documented datasets with ownership, lineage, and access controls.
- Implement data-quality and observability checks for freshness, completeness, distributions, and schema drift with alerting.
- Monitor owned pipelines, respond to incidents, perform root-cause analysis, and iteratively improve production systems.
- Own agentic harnesses and loop- and graph-based workflows for feature creation, freshness, and data-quality monitoring.
- Collaborate with the Head of Data Engineering, Head of ML, and InPost executive team as the technical authority for the data engineering function.
Requirements
- 5+ years of experience in data engineering, including pipelines and data modeling.
- 3+ years of experience with Spark, Databricks, or an equivalent lakehouse.
- 5+ years using SQL, data modeling, and Python for data engineering.
- 2+ years of advanced agentic coding tools and loop engineering, including context and tool design, evaluations, guardrails, and iteration.
- Experience designing batch and streaming pipelines on lakehouse architectures.
- Experience with dimensional and event-based data modeling for analytical and ML consumption.
- Experience with SQL performance tuning and partitioning on large tables.
- Experience with Python, Spark, and orchestration tools such as Airflow or Workflows.
- Experience with agentic tooling, loop and graph engineering, evaluation and guardrail design, and LLMs.
- Understanding of data lineage, cataloging, governance, and GDPR-aware data practices.
- English proficiency at B2 level or above.
- University degree in Computer Science, Data Engineering, Mathematics, or a related field.
- Preferred: 1+ year with a feature store or ML data serving.
- Preferred: 1+ year with payments, fintech, or lending data.
- Preferred: 1+ year with streaming, including Kafka or Structured Streaming.
- Preferred: experience with payment or lending data, credit-risk feature design, real-time streaming and CDC patterns, dbt or equivalent transformation frameworks, and Databricks cost and performance optimization.
Benefits
- Opportunity to work in a diverse, international, and cross-functional environment with leading experts.
- Work and learn with senior data leadership and their leadership team, with opportunities for development and exposure across e-commerce and logistics.
- Flexible hours and an excellent working environment.
- Training and development opportunities within a growing company operating across several European locations.
- B2B type of contract.
- Individual-contributor role with organization-wide scope and direct collaboration with data, ML, and executive leadership.
Tech Stack
Categories
AI ApplicationsData Engineering
About InPost
InPost is a public logistics company listed on Euronext Amsterdam that builds and operates an out-of-home parcel delivery network for e-commerce. Its network includes more than 50,000 out-of-home pick-up points, including over 27,000 parcel lockers, plus to-door courier and fulfillment services for online merchants across Poland, the UK, Italy, Iberia, France and Benelux. Headquartered in Luxembourg, it acquired French operator Mondial Relay in 2021.