Data Engineer
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid · 3 days in office
- Category
- Data engineering
Technologies & skills
What you'll be doing
Join a Dublin-based technology organization as a Data Engineer, focusing on designing and maintaining data platforms, pipelines, and integrations for analytics and AI-driven solutions. Collaborate with engineering, AI/ML, and DevOps teams to deliver scalable, high-quality data assets in a hybrid work environment.
- Designing and maintaining scalable ETL/ELT and data ingestion pipelines
- Transforming structured, semi-structured, and unstructured data into high-quality datasets
- Developing data models, validation frameworks, and quality controls
- Integrating data from APIs, cloud platforms, internal systems, and third-party sources
- Supporting data governance, security, compliance, and documentation standards
- Collaborating with cross-functional engineering and data teams to ensure reliable data availability
Key requirements
Must-have
- 2-3 years experience in a similar role
- Strong commercial experience in Data Engineering
- Advanced SQL and Python development skills
- Expertise in data ingestion, transformation, data modelling, and pipeline optimisation
- Experience working with APIs, cloud-based data environments, data warehouses, or lakehouses
- Exposure to workflow orchestration and systems integration
- Understanding of data governance, security, privacy, and compliance best practices
- Irish/EU Citizenship
Nice-to-have
- Knowledge of Databricks
- Experience with Go (Golang)
Experience: 2-3 years in a similar data engineering role
Role signals
- Technical focus
- data engineering, data pipelines, integrations
- Hands-on vs management
- Hands-on
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Full job description
Data Engineer
Dublin | Hybrid (3 days per week)
- Cpl are partnering with an innovative and rapidly growing technology organisation to hire a Data Engineer for their Dublin-based team.
- This is an exciting opportunity to join a high-performing engineering environment where data is at the heart of product development, analytics, and AI-driven solutions.
- The successful candidate will play a key role in designing and maintaining the data platforms, pipelines, and integrations that power critical business applications.
- Working closely with software engineers, AI/ML specialists, and DevOps teams, you will help transform complex datasets into trusted, scalable, and actionable data assets.
The Role
As a Data Engineer, you will be responsible for building and supporting robust data infrastructure that enables advanced analytics, reporting, and machine learning initiatives.
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Key responsibilities include:
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Designing and maintaining scalable ETL/ELT and data ingestion pipelines.
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Transforming structured, semi-structured, and unstructured data into high-quality datasets.
-
Developing data models, validation frameworks, and quality controls.
-
Integrating data from APIs, cloud platforms, internal systems, and third-party sources.
-
Supporting data governance, security, compliance, and documentation standards.
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Collaborating with cross-functional engineering and data teams to ensure reliable data availability across business-critical platforms.
Skills & Experience
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We are looking for candidates with:
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2-3 Years experience in a similar role
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Strong commercial experience in Data Engineering.
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Advanced SQL and Python development skills.
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Expertise in data ingestion, transformation, data modelling, and pipeline optimisation.
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Experience working with APIs, cloud-based data environments, data warehouses, or lakehouses.
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Exposure to workflow orchestration and systems integration.
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Knowledge of Databricks would be advantageous.
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Experience with Go (Golang) is beneficial but not essential.
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Understanding of data governance, security, privacy, and compliance best practices.
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Must have Irish/EU Citizenship
What's on Offer
- Opportunity to work on a modern data platform supporting AI and analytics initiatives.
- Exposure to cutting-edge technology and large-scale data challenges.
- Collaborative engineering culture with strong cross-functional engagement.
- Hybrid working model based in Dublin.
- Significant opportunities for learning, growth, and career progression within a scaling technology organisation.
Key Technologies
- Python
- SQL
- Go (Golang)
- APIs & Integrations
- Cloud Data Platforms
- ETL / ELT
- Data Modelling
- Databricks (desirable
#LI-JM2
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Data Engineer role in Dublin focuses on building and maintaining robust data pipelines, integrating diverse data sources, and supporting analytics and AI initiatives within a collaborative, cross-functional engineering environment.
- Hands-on data engineering experience·High
- Technical proficiency in SQL and Python·High
- Experience with data integration and cloud platforms·Medium
- Understanding of data governance and compliance·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Practice Advanced SQL and Python
0–10 minSpend time solving complex SQL queries and Python data transformation exercises relevant to data engineering.
Prepare STAR Stories for Key Projects
10–17 minOutline 2-3 examples of your work on ETL pipelines, data integration, and data modelling using the STAR method.
Research Databricks and Cloud Data Platforms
17–22 minRead documentation or case studies on Databricks and similar platforms to discuss their features confidently.
Review Data Governance and Compliance Concepts
22–26 minRefresh your understanding of data governance, security, and compliance frameworks relevant to the role.
Prepare Role-Specific Questions
26–30 minSelect and tailor 2-3 thoughtful questions to ask the interviewer about the team, projects, and growth.
Talking points
, 5 itemsDesigning and Maintaining ETL/ELT Pipelines
You should be ready to discuss your experience building scalable and reliable data pipelines, as this is a core responsibility.
Advanced SQL and Python Skills
Demonstrating strong technical skills in SQL and Python is essential, as these are key technologies for the role.
Integrating Data from Multiple Sources
Prepare examples of integrating APIs, cloud platforms, and internal systems, as the role requires handling diverse data sources.
Data Modelling and Quality Controls
Be ready to explain how you have developed data models and implemented validation frameworks to ensure data quality.
Data Governance, Security, and Compliance
Show your understanding of best practices in data governance and compliance, as supporting these standards is part of the job.
What to research
, 5 itemsReview Advanced SQL and Python Techniques
Refresh your knowledge of complex SQL queries, data transformations, and Python scripting for data engineering tasks.
Understand ETL/ELT Pipeline Design
Be ready to discuss your approach to designing, optimising, and maintaining scalable data pipelines.
Familiarise with Databricks and Cloud Data Platforms
If you have experience, review your work with Databricks or similar platforms; otherwise, research their core features and use cases.
Prepare Examples of Data Integration and Modelling
Gather examples where you integrated data from APIs, cloud sources, or internal systems and developed robust data models.
Review Data Governance and Compliance Practices
Be prepared to discuss how you have supported data governance, security, and compliance in previous roles.
Questions to ask
, 6 itemsWhat are the main data platforms and tools currently used by the team?
Why ask this? To understand the technical environment and where your skills will be most applicable.
How does the team approach data governance, security, and compliance?
Why ask this? To clarify expectations and processes around data management best practices.
Can you describe a typical cross-functional project involving data engineering, AI/ML, and DevOps?
Why ask this? To learn about collaboration and the types of projects you will be involved in.
What are the biggest data challenges the team is currently facing?
Why ask this? To identify key priorities and areas where you can add value.
How is success measured for data engineering roles within the organisation?
Why ask this? To understand performance expectations and growth opportunities.
What opportunities are there for learning and professional development?
Why ask this? To gauge the company's commitment to ongoing skill development.
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