Azure Data Engineer - Python/PySpark, SQL, Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Data Lake Storage, Azure Key Vault, ETL/ELT,

Locations: Stockholm or Malmö, with Helsingborg as an optional base

Work Model: Hybrid, 2–3 days per week onsite

Start: ASAP

Contract: 12 months (freelance)

Our client, a highly regarded technology company shaping modern data-driven products, is looking for an experienced Azure Data Engineer to join their team. This role centres on building robust, scalable, cloud-native data solutions that empower analytics, business decisions, and long-term digital strategy.

Key Responsibilities

Design and build data solutions

Create and maintain scalable data pipelines and cloud architectures using a wide range of Azure services.

Develop ETL/ELT processes

Design, manage, and optimise ETL and ELT workflows, particularly using Azure Data Factory and related tools.

Manage data storage

Implement and oversee data storage platforms such as Azure SQL Database, Azure Data Lake Storage (ADLS), and Azure Cosmos DB.

Ensure data quality

Apply validation, cleansing, and quality assurance methods to maintain strong data integrity across pipelines and models.

Optimise performance

Monitor, diagnose, and resolve performance issues across data pipelines, transformations, and databases.

Collaborate with stakeholders

Work alongside data scientists, analysts, engineering teams, and business stakeholders to understand data needs and deliver clear, scalable solutions.

Ensure data security

Implement access controls and security measures within the Azure ecosystem, leveraging services such as Azure Key Vault.

Required Skills & Experience

Azure expertise

Hands-on proficiency with Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Data Lake Storage, Azure Key Vault, and related cloud components.

Programming & scripting

Strong experience with Python, SQL, and PySpark for data transformation, automation, and analysis.

Data modelling

Ability to design, maintain, and optimise data models that ensure consistency, scalability, and performance.

Cloud & data warehousing concepts

Solid understanding of cloud data architectures, data warehousing principles, and modern data engineering patterns.

Analytical ability

Comfort working with large datasets, investigating complex data behaviour, and supporting analytical teams.