Credit Data Engineer

PLP Group; Milano; Ibrido; EUR 90.000 - 120.000; Tempo pieno

Descrizione del lavoro

PLP Group in Milano seeks a Data Platform Owner to own the global underwriting tables—canonical facts, dimensions, and data contracts with clear SLAs for freshness, completeness and accuracy. You’ll design AI-friendly schemas and metadata, enabling both humans and agents to reason over data.

You’ll build and run batch and streaming pipelines feeding UW scoring, real-time decisioning, monitoring, and governance reporting, ensuring observability, reconciliation, and incident reviews while aligning

Competenze

• Experience owning mission-critical data products.
• Strong data modeling and schema evolution skills.
• Familiarity with AI/agent patterns and data contracts.

Mansioni

• Own the global UW tables: canonical facts and dimensions with data lineage.
• Design AI-friendly schemas, metadata, and data contracts.
• Build + run batch and streaming pipelines for scoring, decisioning, and reporting.
• Ensure observability, alerts, audits, and backfills; drive incident reviews.

Conoscenze

Data ownership

Observability

AI patterns

Strumenti

SQL

PySpark

Airflow

Kafka

Redshift

AWS

Descrizione del lavoro

What you’ll do

Own the global UW tables (canonical facts/dimensions for applications, decisions, features, repayments, delinquency) with clear SLAs for freshness, completeness, accuracy, and data lineage. Design for AI-agents and humans: consistent IDs, canonical events, explicit metric definitions, rich metadata (schemas, data dictionaries), and machine-readable data contracts. Build & run pipelines (batch + streaming) that feed UW scoring, real-time decisioning, monitoring, and underwriting optimization. Instrument quality & observability (alerts, audits, reconciliation, backfills) and drive incident/root-cause reviews. Partner closely with Credit Portfolio Management, Policy teams, Modeling teams, and treasury and finance teams to land features for RUE and consumer-centric models, plus regulatory and management reporting.

Tech stack (what we use)

Languages: SQL, PySpark, Python Frameworks: Apache Airflow, AWS Glue, Kafka, Redshift Cloud & DevOps: AWS (S3, Lambda, CloudWatch, SNS/SQS, Kinesis), Terraform; Git; CI/CD

What you’ll bring

Proven ownership of mission-critical data products (batch + streaming). Data modeling, schema evolution, data contracts, and strong observability chops. Familiarity with AI/agent patterns (agent-friendly schemas/endpoints, embeddings/vector search).