Senior Data Engineer

Role: Senior Data Engineer

Job Ref Id: JD-1292

Competence level: 5+ years

Start date: 2026-11-09

End date: 2027-04-30

Extension option: Yes

Scope: 100%

Location: Stockholm

Remote work: On-site; remote considered

Language: English

Application deadline: 2026-11-01

Number of consultants: 1

Security clearance: Not stated

About The Assignment We are looking for a Senior Data Engineer for an interim position in our Trust Intelligence & Calling department. Note: We prefer on-site but we're considering remote candidate as well. We are re-architecting our data pipelines to meet stricter requirements on user-data lifecycle: encryption, retention, and deletion across historical and derived data. You lead the technical migration and establish the engineering processes that keep the pipelines reliable after it.

Responsibilities

• Propose and drive the architecture for user-data handling across Airflow, Spark, dbt, and BigQuery: encryption points, derived tables, backfills, partitions.
• Migrate existing pipelines one at a time without changing their output, behind a regression harness you build.
• Design the processes the team keeps after the migration: pipeline change review, testing standards, incident post-mortems, release checklist.
• Remove duplicated steps and copy-pasted logic as you pass through each pipeline.
• Build verification jobs that prove the data-lifecycle rules hold end to end.
• Pair with and mentor a data engineer on the team, with the goal that the team owns the migrated pipelines independently.
• Share pipeline maintenance and incident handling; fix root causes.
• Document decisions as short architecture decision records.

Requirements Mandatory

• Deep hands-on experience with Airflow, Spark, dbt, BigQuery, and Python.
• At least one pipeline migration you led technically from start to finish.
• Experience refactoring a live pipeline while proving its output is unchanged.
• Experience introducing engineering processes (testing, review, incident handling) to a team and making them stick.
• Mentoring experience with a concrete outcome.

Nice-to-have

• A prior project on encryption at rest, data retention, or deletion compliance (for example GDPR).
• Envelope encryption with a key manager (for example GCP KMS), BigQuery AEAD functions, or Tink.
• BigQuery column-level security, policy tags, Data Catalog.
• Config Connector (KCC) for managing GCP resources from Kubernetes.
• Events with Kafka.