Credit Data Engineer

Klarna • Python • SQL • 7 months ago
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Salary
$74k – $82k per year
Location
In office (Milan)
Employment
Full-Time
Overview
Company
Impact
Profile match

Klarna

Klarna is a Swedish financial technology company founded in Stockholm in 2005 that provides payment and shopping services to consumers and merchants worldwide. Its core products let shoppers pay immediately, defer a payment or split a purchase into interest-free instalments, while merchants receive settlement, fraud protection and conversion tooling through a single checkout integration. The group has expanded into a shopping app with price comparison, cashback and a bank offering, holds a European banking licence and listed its shares on the New York Stock Exchange in 2025.
klarna.com • HQ: Stockholm, Sweden • Commerce • Financial Services • Lending • FinTech • Payments • 1001-5000 employees • Est. 2005

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).

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