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# Data Engineer (Azure Databricks) in the EU

- **Company:** Andersen Software
- **Location:** Austria (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Microsoft Windows, Data Analysis, Audit Trail, Automation of Tests, Microsoft Azure, BigTable, Continuous Integration, Data Validation, Information Engineering, Data Governance, Data Mart, Data Systems, Data Warehousing, DevOps, Metadata, Performance Tuning, Power BI, Microsoft SharePoint, Systems Integration, Data Logging, Data Ingestion, Delivery Pipeline, Apache Spark, Git, Data Lakes, Git Flow, Data Lineage, SAP S/4HANA, Machine Learning Operations, Api Design, Data Pipelines, GXP, Databricks
- **Published:** October 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7272423449e42502

## About the Role

* Commercial experience in Data Engineering for 4+ years.
 * Strong hands-on experience with Azure Databricks, including Spark and Delta Lake. Experience building and maintaining multi-layer data pipelines following the Landing Raw Enriched
 * Curated architecture.
 * Experience with schema enforcement and schema-drift handling.
 * Ability to implement data quality checks at ingestion.
 * Experience orchestrating pipelines using Databricks Jobs / Workflows, including dependencies, retries, alerts, and failure handling.
 * Experience with both batch and API-based data ingestion.
 * Strong hands-on experience with dbt.
 * Ability to monitor job failures, data freshness, and overall pipeline health.
 * Experience delivering data in line with defined SLAs.
 * Experience implementing alerting, logging, and retry strategies.
 * Experience designing and maintaining access control models.
 * Understanding of audit trails, data lineage, and end-to-end traceability.
 * Experience with logging and evidence generation for audit and compliance purposes.
 * Understanding of segregation of duties.
 * Experience defining and maintaining data contracts.
 * Ability to create and maintain clear pipeline documentation.
 * Experience working across controlled Dev, Test, and Prod environments.
 * Ability to ensure traceability between requirements and implemented data solutions.
 * Level of English - from Upper-Intermediate and above.

Desired skills

 * Experience with Databricks.
 * Experience with SAP S/4HANA; Microsoft 365; Azure; SharePoint; Enterprise integrations.
 * Experience with CI/CD and DevOps practices for data, including Git-based development and branching strategies.
 * Experience building and maintaining CI/CD pipelines for data workflows.
 * Experience promoting code and data solutions across Dev, Test, and Prod environments.
 * Ability to integrate automated testing into deployment pipelines.
 * Experience with MLOps and AI pipelines on Databricks.
 * Exposure to regulated domains such as GxP, clinical trials, and pharmacovigilance.
 * Strong SQL engineering skills, including the development of modular and reusable dbt models, tests, and documentation at Analytics Engineer depth.
 * Experience with dimensional data modeling, including star schemas, One Big Table (OBT) approaches, and curated data marts.
 * Familiarity with Tabular Editor and metadata-as-code concepts.
 * Experience with Power BI dataset design, performance optimization, and maintaining KPI and semantic consistency.
 * Experience in the pharmaceutical or biotechnology industry.

## Description

Building and maintaining multi-layer pipelines (Landing Raw Enriched

 * Curated) on Azure Databricks (Spark, Delta Lake),
 * Orchestrating pipelines via Databricks Jobs / Workflows: dependencies, retries, alerting.
 * Implementing batch and API-based data ingestion from multiple source systems.
 * Applying schema enforcement and handle schema drift; run data quality checks at ingestion.
 * Working hands-on with dbt for pipeline observability and data governance tasks.
 * Monitoring job failures, data freshness, and pipeline health against SLA-based delivery targets.
 * Setting up alerts, logging, and retry strategies for production pipelines.
 * Working within a controlled Dev/Test/Prod environment: access control, audit trails, pipeline documentation, traceability to requirements.
 * Contributing to CI/CD and MLOps/AI pipelines on Databricks where the project needs it.