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TRCI-26-05421
Data Engineer (Azure Databricks / Lakehouse)
Contract Data Engineer needed in Malmö (5–10 years) to design, build, and operate scalable, automated data products for performance evaluation across IKEA franchisees. The role covers the full data engineering lifecycle (architecture/design through deployment and production support) with a strong focus on Azure-based lakehouse solutions using Databricks (Spark/Delta), robust data validation and auditability, performance optimization, and DevOps/CI/CD practices (Git, automated tests, IaC).
Position summary
- Location
- Sweden
- Workplace
- On-site
- Employment
- Contract
- Experience
- Minimum 5 years and Maximum 10 years
Role overview
Why This Role Matters.
Contract Data Engineer needed in Malmö (5–10 years) to design, build, and operate scalable, automated data products for performance evaluation across IKEA franchisees. The role covers the full data engineering lifecycle (architecture/design through deployment and production support) with a strong focus on Azure-based lakehouse solutions using Databricks (Spark/Delta), robust data validation and auditability, performance optimization, and DevOps/CI/CD practices (Git, automated tests, IaC).
Your Impact
Deliver Enterprise Value
Help organisations solve complex business problems through modern technology, consulting expertise and measurable outcomes.
Collaboration
Work Across Teams
Collaborate with consultants, architects, engineers and client stakeholders throughout the project lifecycle.
Growth
Learn Continuously
Gain exposure to enterprise technologies, certifications, mentoring and real-world project experience.
Career Path
Grow With Ubique
Build a long-term consulting career with opportunities to take on greater responsibility and leadership over time.
Responsibilities
What You'll Be Doing.
Every role at Ubique contributes directly to solving meaningful business challenges for our clients.
02
Join us in shaping the future of a data-driven IKEA!
03
As a Data Engineer you will play a key role in developing CMP and Franchisee Performance Evaluations
04
Strong understanding of data engineering guidelines, release processes, and quality expectations, including data validation, performance optimization, monitoring, and troubleshooting in production environments.
05
Passionate about building clean, scalable, resilient, and cost efficient data solutions using cloud native architectures and modern data platforms, with a strong focus on maintainability and reusability.
06
Proven experience designing and implementing end to end data pipelines (batch and streaming), including ingestion, transformation, and serving layers, using best practices in data modelling and lakehouse architecture.
07
Hands on expertise with Databricks, including Apache Spark, Delta Lake, job orchestration, performance tuning, and environment management.
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Strong knowledge of Microsoft Azure, particularly services commonly used in data platforms such as Azure Data Lake Storage,etc
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Solid experience with DevOps practices, including source control (e.g., Git), CI/CD pipelines, automated testing, environment promotion, and infrastructure as code for data platforms.
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Strong communication skills, with the ability to clearly explain data architectures, pipelines, and trade offs to non technical stakeholders.
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Databricks (Apache Spark, Delta Lake, notebooks, job orchestration, performance optimization)
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Microsoft Azure (e.g., Azure Data Lake Storage)
Technology stack
Tools & Technologies.
The platforms and technologies you'll use to build modern, enterprise-grade solutions.
01
Databricks
Required
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Apache Spark
Required
03
Delta Lake
Required
04
Lakehouse
Required
05
Microsoft Azure
Required
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Azure Data Lake Storage
Required
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ADLS
Required
08
CI/CD
Required
09
DevOps
Required
10
Git
Required
11
Infrastructure as code
Required
12
Automated testing
Required
13
Job orchestration
Required
14
Notebooks
Required
15
Data pipelines
Required
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Batch processing
Required
17
Streaming
Required
18
Data modelling
Required
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Data validation
Required
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Monitoring
Required
Requirements
Skills & Experience.
We value curiosity, collaboration and continuous learning. If you don't meet every requirement but believe you can make an impact, we'd still love to hear from you.
Essential
Required Qualifications
Data engineering (end-to-end pipelines: ingestion, transformation, serving)
Databricks
Apache Spark
Delta Lake
Lakehouse architecture
Microsoft Azure
Azure Data Lake Storage (ADLS)
Data modelling
Data validation / data quality
Job orchestration
Performance optimization / tuning
Production monitoring and troubleshooting
DevOps practices for data platforms
Git (source control)
CI/CD pipelines
Automated testing
Infrastructure as code (IaC)
Solution design and architecture (high-level and low-level)
Requirements specification
Documentation and deployment
Preferred
Nice to Have
Streaming pipelines (in addition to batch)
Release process ownership / environment promotion practices
Cost optimization in cloud data platforms
Governance and auditability patterns (beyond basic validation)
Agile ceremonies (sprint planning/estimation)
What you'll gain
More Than Just A Job.
We're committed to helping every team member grow professionally, personally and technically while working on meaningful projects.
Global Exposure
Collaborate with international clients and multicultural teams on enterprise programmes.
Continuous Learning
Expand your expertise through mentoring, certifications and hands-on project experience.
Career Growth
Take ownership, develop leadership skills and grow your consulting career over time.
Flexible Working
Hybrid and remote collaboration designed around trust and delivering exceptional outcomes.
People First
Join a supportive culture where collaboration, respect and long-term relationships come first.
Enterprise Projects
Work on meaningful technology initiatives for leading organisations across industries.
Apply
Apply for Data Engineer (Azure Databricks / Lakehouse)
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