About this role
Senior Data Engineer / Data Pipeline Architect
Location: Stockholm, Sweden (Hybrid – Travel Required)
Experience Required: 5 to 10 years
Duration: 6 months (Extendable)
Employment Type: FTE
Working Mode: Hybrid (Travel Required)
Language: English
Start: November 9, 2026 – April 30, 2027
We are looking for an experienced Senior Data Engineer / Data Pipeline Architect for an assignment in Stockholm focusing on data pipeline modernization, data security, and technical architecture .
The assignment involves leading the technical migration of existing data pipelines to meet stricter requirements for encryption, data retention, and deletion of user data , including historical and derived datasets.
Key Responsibilities
- Design and drive the architecture, migrating pipelines incrementally while ensuring existing outputs remain unchanged .
- Establish sustainable engineering processes for testing, code reviews, incident management, and releases .
- Combine architectural leadership with hands-on engineering using Airflow, Spark, dbt, BigQuery, and Python.
- Collaborate closely with and mentor a Data Engineer within the team, ensuring long-term ownership, reliability, and independent maintenance of the migrated pipelines.
Must-Have Requirements
- Minimum 5 years of relevant professional experience (5–10 years overall).
- Deep hands-on experience with Airflow, Spark, dbt, BigQuery, and Python.
- Proven experience technically leading at least one complete data pipeline migration from start to finish.
- Experience refactoring production data pipelines while verifying that their outputs remain unchanged.
- Experience establishing and implementing engineering processes for testing, code reviews, and incident management.
- Demonstrated mentoring experience with measurable outcomes .
Education
- Minimum Bachelor's degree (or equivalent).
Nice-to-Have
- Experience with encryption at rest, data retention, or data deletion compliance (e.g., GDPR).
- Experience with envelope encryption and key management solutions (e.g., GCP KMS, AEAD functions, or Tink).
- Knowledge of BigQuery column-level security, policy tags, and Data Catalog .
- Experience with Config Connector (KCC) for managing GCP resources through Kubernetes.
- Experience with event-driven architectures using Kafka .