Data Engineer

Your Role

As a Senior Databricks Lakehouse Architect (Swedish-Speaking), you will lead the design and evolution of enterprise-grade data platforms built on the modern lakehouse paradigm. This role is tailored for engineers who go beyond standard pipeline development focusing on performance tuning, governance at scale, and advanced Databricks capabilities.

You will act as a technical authority, shaping best practices across data engineering, platform architecture, and machine learning integration within the Databricks ecosystem.



In This Role, You Will

Own end-to-end Lakehouse architecture
Design and implement scalable lakehouse solutions using Databricks, leveraging advanced features such as Delta Live Tables, Unity Catalog, and Photon for high-performance workloads.

Build highly optimized Spark workloads
Develop and fine-tune complex distributed data pipelines using Apache Spark, with deep focus on query optimisation, partitioning strategies, and cost efficiency.

Implement advanced data governance and security
Drive enterprise-grade governance using Unity Catalog, data lineage, fine-grained access control, and compliance frameworks across Azure environments.

Engineer real-time and batch data solutions
Design streaming architectures (Structured Streaming, Auto Loader) alongside batch processing pipelines for large-scale, mission-critical data sets.

Lead ML and MLOps integration
Operational machine learning models using Databricks ML, MLflow, and CI/CD pipelines-ensuring reproducibility, monitoring, and life cycle management.

Act as a technical mentor and stakeholder partner
Collaborate with senior stakeholders, data scientists, and platform teams while mentoring engineers and setting engineering standards.



Your Profile

* Extensive hands-on experience in Data Engineering, with a strong focus on Databricks-based platforms
* Deep expertise in:

* Advanced Databricks Lakehouse architecture
* Delta Lake internals (transaction logs, optimisation, Z-ordering, vacuum strategies)
* Performance tuning in Spark (Catalyst optimiser, execution plans, memory management)

* Proven experience with Azure Data Lake Storage, Azure Data Factory, and broader Azure ecosystem
* Strong programming skills in Python and SQL, with production-grade pipeline development experience
* Hands-on experience with MLflow, feature stores, and MLOps practices
* Experience implementing data governance frameworks and working with regulated data environments
* Fluent Swedish (spoken and written) - essential for collaboration with local stakeholders



What Sets You Apart (Essesntial)

* Experience with Unity Catalog at scale across multiple workspaces
* Deep understanding of cost optimization in Databricks (cluster policies, spot instances, workload isolation)
* Experience with Delta Sharing and cross-platform data collaboration
* Building medallion architecture (Bronze/Silver/Gold) at enterprise scale
* Familiarity with infrastructure-as-code (Terraform) for Databricks deployments
* Exposure to real-time analytics and event-driven architectures