Data engineer

Klarna Bank AB; Stockholms kommun; On-site; SEK 600,000 - 800,000; Full time

Job summary

A leading financial institution in Sweden is seeking a data engineer to own global underwriting tables and ensure data quality and observability. The successful candidate will be responsible for designing data processes for AI and human agents while building and managing data pipelines in a cloud environment. Familiarity with SQL, PySpark, and relevant AWS tools is essential. The role offers opportunities for collaboration across multiple teams for consumer-centric model features.

Qualifications

• Proven ownership of mission-critical data products (batch + streaming).
• Familiarity with AI/agent patterns (agent-friendly schemas/endpoints, embeddings/vector search).

Responsibilities

• Own the global UW tables with clear SLAs for freshness and accuracy.
• Design for AI agents and humans, ensuring consistent IDs and rich metadata.
• Build and run pipelines for UW scoring and real-time decisioning.
• Instrument quality and observability for incident reviews.
• Partner with cross-functional teams for feature implementations.

Skills

Data ownership

Data modeling

Schema evolution

Strong observability

SQL

Python

PySpark

Tools

Apache Airflow

AWS Glue

Kafka

Redshift

Terraform

CI/CD

Job description

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).