Data Engineer 4m contract
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At a glance
- Employment type
- Contract
- Workplace
- Hybrid
- Day rate
- €450-500/day (EUR)
- Category
- Data engineering
Technologies & skills
What you'll be doing
4-month contract Data Engineer role (hybrid, Dublin) focused on building and maintaining ETL/ELT pipelines and data workflows using Python, SQL, and Microsoft Azure technologies. Collaborate with engineers and analysts to deliver reliable data solutions and ensure data quality.
- Build and maintain ETL/ELT pipelines using Python and SQL
- Develop data processing workflows in Azure Databricks and Azure Data Factory
- Work with ADLS Gen2, Delta Lake, and Unity Catalog
- Implement data quality checks and troubleshoot pipeline issues
- Collaborate with data engineers and analysts to deliver reliable data solutions
Key requirements
Must-have
- 3–6 years of data engineering experience
- Strong Python and SQL skills
- Hands-on experience with Azure Databricks and ETL/ELT pipelines
- Knowledge of Azure Data Factory, ADLS Gen2, and Delta Lake
- Experience with Git, data modelling, and data quality validation
- Good problem-solving and communication skills
Experience: 3–6 years
Role signals
- Technical focus
- data engineering
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Data Engineer – 4-Month Contract
We’re looking for a hands-on Data Engineer for a 4-month contract to develop and maintain data pipelines using Microsoft Azure technologies.
Hybrid structure in Dublin city centre
Key Responsibilities
-
Build and maintain ETL/ELT pipelines using Python and SQL.
-
Develop data processing workflows in Azure Databricks and Azure Data Factory.
-
Work with ADLS Gen2, Delta Lake and Unity Catalog.
-
Implement data quality checks and troubleshoot pipeline issues.
-
Collaborate with data engineers and analysts to deliver reliable data solutions.
Requirements
-
3–6 years of data engineering experience.
-
Strong Python and SQL skills.
-
Hands-on experience with Azure Databricks and ETL/ELT pipelines.
-
Knowledge of Azure Data Factory, ADLS Gen2 and Delta Lake.
-
Experience with Git, data modelling and data quality validation.
-
Good problem-solving and communication skills.
If you’re available for a 4-month contract and have the relevant technical experience, we’d love to hear from you.
Apply Now
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the role is a hands-on Data Engineer contract focused on building and maintaining ETL/ELT pipelines and data workflows using Azure technologies, Python, and SQL in a hybrid Dublin-based environment.
- Azure Data Engineering Expertise·High
- Hands-on Pipeline Development·High
- Data Quality and Troubleshooting·Medium
- Team Collaboration·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Recent Azure Data Engineering Projects
0–8 minSelect 1-2 relevant projects where you used Databricks, Data Factory, or similar Azure tools to discuss in detail.
Refresh Python and SQL ETL/ELT Examples
8–15 minPrepare concise stories highlighting your pipeline development, focusing on challenges and outcomes.
Summarize Data Quality and Validation Approaches
15–20 minList specific techniques and tools you have used to ensure data quality in past roles.
Research the Team’s Azure Stack and Practices
20–25 minPrepare targeted questions about their use of ADLS Gen2, Delta Lake, and Unity Catalog.
Plan Questions and Closing Points
25–30 minDraft 2-3 thoughtful questions to ask and key points to emphasize about your fit for a short-term, hands-on contract.
Talking points
, 5 itemsBuilding ETL/ELT Pipelines with Python and SQL
Demonstrates your ability to design and implement robust data pipelines, a core responsibility for this contract.
Hands-on Experience with Azure Databricks and Data Factory
Shows your familiarity with the specific Azure tools required for developing and orchestrating data workflows.
Working with ADLS Gen2, Delta Lake, and Unity Catalog
Highlights your experience managing data storage, versioning, and governance in the Azure ecosystem.
Implementing Data Quality Checks and Troubleshooting
Demonstrates your approach to ensuring reliable data and resolving pipeline issues, which is essential for delivering quality solutions.
Collaboration with Data Engineers and Analysts
Shows your ability to work effectively in a team to deliver data solutions, as collaboration is emphasized in the listing.
What to research
, 4 itemsAzure Databricks and Data Factory Workflows
Review your experience building and orchestrating data workflows using these tools, focusing on practical examples.
ETL/ELT Pipeline Design with Python and SQL
Prepare to discuss your approach to designing, implementing, and optimizing pipelines using these languages.
Data Quality Validation Techniques
Be ready to explain the methods and tools you use to ensure data quality and handle validation in pipelines.
Working with ADLS Gen2, Delta Lake, and Unity Catalog
Refresh your knowledge and examples of managing data storage, versioning, and governance in Azure.
Questions to ask
, 6 itemsWhat are the main data sources and destinations involved in the current pipelines?
Why ask this? Clarifies the technical landscape and helps you relate your experience to their environment.
What are the biggest challenges the team is currently facing with data pipelines or workflows?
Why ask this? Shows your interest in problem-solving and readiness to contribute quickly.
How is success measured for this contract role over the 4-month period?
Why ask this? Helps you understand expectations and deliverables.
What is the typical collaboration process between data engineers and analysts here?
Why ask this? Gives insight into team dynamics and communication.
Are there any upcoming projects or migrations involving Azure data services?
Why ask this? Identifies potential opportunities to add value and areas where your skills are most needed.
What tools or processes are in place for monitoring and troubleshooting pipeline issues?
Why ask this? Shows your proactive approach to maintaining data reliability.
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