Senior Data Engineer - Cloud Migrations & Databricks Expert

SQUER Solutions GmbH; Wien; Hybrid; EUR 75,000 - 95,000; Full time

Benefits offered by this job

Hybrid work options

Annual training budget €5,000

Leadership Academy

40h/year personal education leave

AI tools suite (Anthropic, OpenAI)

Free lunch in office

MyClubs sports membership

Klimaticket contribution

Job summary

SQUER Solutions GmbH is seeking a Senior Data Engineer in Austria to lead large-scale migrations from legacy systems to cloud/lakehouse architectures. You will design Databricks pipelines using Spark, Delta Lake, Unity Catalog, and CI/CD-like tooling, while owning data modeling and cross-system reconciliation.

Expect to drive discovery and assessment, translate ambiguous specs into concrete requirements, and establish data quality and automated validation practices.

Qualifications

• 5+ years in data engineering with large-scale migrations to cloud/lakehouse.
• Hands-on mastery of Databricks ecosystem (Spark, Delta Lake, Unity Catalog).
• Strong data modeling and schema mapping across heterogeneous systems.
• Experience in end-to-end discovery/assessment, extracting concrete requirements.

Responsibilities

• Lead migration projects from legacy systems to cloud/lakehouse.
• Design and implement robust data pipelines in Databricks.
• Own data modeling, mapping, and reconciliation across systems.

Skills

Migration track record

Databricks depth

Data modeling & reconciliation

Discovery experience

Data quality rigor

Communication & risk ownership

AI-assisted workflows

Tools

Spark

Delta Lake

Unity Catalog

Lakeflow Declarative Pipelines

Asset Bundles

Job description

SQUER Solutions GmbH is seeking a Senior Data Engineer in Austria to lead large-scale migrations from legacy systems to cloud/lakehouse architectures. You will design Databricks pipelines using Spark, Delta Lake, Unity Catalog, and CI/CD-like tooling, while owning data modeling and cross-system reconciliation.

Expect to drive discovery and assessment, translate ambiguous specs into concrete requirements, and establish data quality and automated validation practices.