Data Engineer AWS, Python & Data Pipelines - 206022

Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry.
Job Overview
Assignment Description
We are looking for a Data Engineer to design, build, test, deploy and operate secure and reliable data pipelines and engineering services on AWS.
This is a hands-on software and data engineering role focused on production code, cloud infrastructure, automation and service reliability — not data analysis or dashboard development.
You will work within a cross-functional engineering team, building data ingestion, processing and integration services that support business applications, credit operations, reporting and AI workloads.
What You Will Work On
Build and maintain production data pipelines using Python, Spark, APIs and Kafka.
Develop batch and streaming data ingestion services and reusable components.
Handle schema changes, incremental loads, failures, retries and data reprocessing.
Provision and configure AWS infrastructure using Terraform, including S3, IAM and EKS-related workloads.
Implement automated testing, CI/CD, data validation and reconciliation checks.
Monitor production pipelines, investigate incidents and improve service reliability.
Maintain alerts, runbooks and recovery procedures.
Apply security controls such as least-privilege access, encryption and secrets management.
Optimise pipeline performance, resource usage and cloud costs.
Document technical solutions, interfaces, dependencies and data lineage.
Use approved AI-assisted engineering tools responsibly.

What You Bring
Demonstrated experience building, deploying and supporting production data pipelines or data-platform services.
Practical experience with Python and AWS.
Strong software engineering skills, including Git, code reviews, automated testing and CI/CD.
Experience with Spark, Kafka and API-based data ingestion.
Knowledge of AWS services, including S3, IAM, encryption, logging and networking.
Experience with Terraform and infrastructure automation.
Understanding of Kubernetes/EKS and containerised workloads where required.
Experience with Airflow orchestration and production monitoring.
Good knowledge of SQL, data schemas, file formats and technical data models.
Understanding of data quality, observability, recovery and performance optimisation.
Fluent English and strong technical communication skills.
Degree in Computer Science, Engineering or a related discipline, or equivalent practical experience.

Meritorious
Banking and financial data experience.
Relevant AWS certifications.
Experience with legacy data migrations.
Experience with dbt.
Experience with large-scale batch and streaming data platforms.