Data Engineer
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid
- Salary
- €75k-95k/year (EUR)
- Category
- Data engineering
Technologies & skills
What you'll be doing
Seeking a Data Engineer to build and deploy data pipelines for a business-critical platform using Snowflake, Airflow, Databricks, Python, SQL, and AWS. Requires active code contribution, peer reviews, and strong communication skills. Hybrid role based in Ireland.
Key requirements
Must-have
- Minimum of 5 years' experience creating and deploying data pipelines
- Experience with Snowflake, Airflow, Databricks, PySpark
- 2+ years of experience in Python, SQL, and AWS
- Understanding of data engineering principles and practices
- Strong communication skills and teamwork
Experience: 5+ years
Role signals
- Technical focus
- data engineering
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Data Engineer
My client is looking for someone to actively contribute to the codebase and to participate in peer reviews. You will be building a data processing platform using technologies like Snowflake, Airflow, AWS, Python, PySpark, SQL and Databricks. You'll design, build and deploy data pipelines for a business-critical platform.
- What you Need:
Understand and implement data engineering principles and practices.
Minimum of 5 years' experience creating pipelines and deploying them.
A good background in Snowflake, Airflow, Databricks and PySpark.
2+ Years of experience in Python, SQL and AWS
Communication skills and ability to operate within a team and share views clearly
-
What's on Offer:
- Competitive salary of €75,000-€95,000 plus benefits
-
Hybrid working from a Dublin office
-
Ownership of a modern cloud data stack on systems that matter to the business
Interested? Apply below or Contact Ashton Simms at Reperio Human Capital
Reperio Human Capital acts as an Employment Agency and an Employment Business.
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 position focused on building and deploying data pipelines using Snowflake, Airflow, Databricks, PySpark, Python, SQL, and AWS within a hybrid Dublin-based team.
- Hands-on experience with modern data stack·High
- Ability to design and deploy robust data pipelines·High
- Strong team collaboration and communication·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Key Projects with Relevant Technologies
0–8 minSelect 2-3 projects involving Snowflake, Airflow, Databricks, or PySpark and prepare concise summaries highlighting your role and impact.
Practice Explaining Data Pipeline Design and Deployment
8–15 minRehearse describing your approach to building, testing, and deploying data pipelines, focusing on technical and business outcomes.
Refresh Python, SQL, and AWS Knowledge
15–21 minReview code samples and documentation for data processing tasks and AWS services you have used.
Prepare Examples of Teamwork and Code Reviews
21–26 minThink of specific instances where you contributed to a team codebase and participated in peer reviews.
Draft Questions for the Interviewer
26–30 minSelect and tailor 2-3 questions from the provided list to ask during your interview.
Talking points
, 5 itemsBuilding and Deploying Data Pipelines
You will need to demonstrate your experience designing, implementing, and maintaining robust data pipelines for business-critical applications.
Working with Modern Data Stack (Snowflake, Databricks, Airflow)
The role requires hands-on expertise with these technologies, so be ready to discuss specific projects and technical decisions involving them.
Programming with Python and SQL
You should be able to provide examples of how you have used Python and SQL to process, transform, and analyze data in previous roles.
Cloud Data Engineering on AWS
Demonstrate your ability to leverage AWS services for data engineering tasks, including deployment and scaling of data solutions.
Peer Reviews and Team Communication
The listing highlights codebase contributions and peer reviews, so prepare to discuss your approach to collaboration and code quality.
What to research
, 4 itemsReview Recent Projects with Snowflake, Airflow, and Databricks
Prepare detailed examples of your work with these platforms, focusing on pipeline design, deployment, and troubleshooting.
Brush Up on Python, SQL, and PySpark Skills
Be ready to discuss and possibly demonstrate your coding abilities in data processing and transformation.
Understand AWS Data Engineering Services
Review how you have used AWS for data storage, processing, and deployment in previous roles.
Prepare Examples of Team Collaboration and Code Reviews
Think of situations where you contributed to a shared codebase and participated in peer reviews.
Questions to ask
, 6 itemsWhat are the main business goals for the data platform I would be working on?
Why ask this? To understand how your work will impact the organization and align your efforts with business priorities.
How is the team structured, and how do team members typically collaborate on projects?
Why ask this? To gauge the working environment and expectations for teamwork and communication.
What are the biggest technical challenges currently facing the data engineering team?
Why ask this? To identify areas where your skills can add value and prepare for potential problem-solving scenarios.
How are code reviews and knowledge sharing handled within the team?
Why ask this? To clarify expectations around code quality, peer feedback, and learning opportunities.
What is the typical lifecycle of a data pipeline project here, from design to deployment?
Why ask this? To understand the workflow and your role at each stage of the process.
Are there opportunities to propose or implement new technologies or approaches within the data stack?
Why ask this? To assess the openness to innovation and your potential influence on technical decisions.
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