Data Engineer
TechJobs.ie Job Insights
At a glance
- Employment type
- Full-time
- Workplace
- Hybrid · 3 days in office
- Category
- Data engineering
Technologies & skills
Other technical 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.
- Design and maintain scalable ETL/ELT and data ingestion pipelines
- Transform structured, semi-structured, and unstructured data into high-quality datasets
- Develop data models, validation frameworks, and quality controls
- Integrate data from APIs, cloud platforms, internal systems, and third-party sources
- Support data governance, security, compliance, and documentation standards
- Collaborate 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, data modelling
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
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 pipeline development·High
- Advanced SQL/Python proficiency·High
- Data integration and modelling·High
- Data governance awareness·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Document Relevant Project Experience
0–8 minList and summarise your most relevant data engineering projects, focusing on pipeline design, data integration, and modelling.
Practice Explaining Technical Concepts
8–14 minPrepare concise explanations of your approach to ETL/ELT, data modelling, and data governance for a non-technical audience.
Brush Up on Key Technologies
14–22 minReview advanced SQL and Python, and research Databricks and cloud data platforms if needed.
Prepare Questions and STAR Stories
22–27 minDraft thoughtful questions for the interviewer and structure your experience examples using the STAR method.
Review Data Governance and Compliance Practices
27–30 minRefresh your understanding of data privacy, security, and compliance frameworks relevant to the role.
Talking points
, 5 itemsDesigning and Optimising ETL/ELT Pipelines
You should be ready to discuss specific examples of building scalable, reliable data pipelines, as this is a core responsibility.
Advanced SQL and Python Skills
Demonstrating your ability to write efficient, production-grade code in SQL and Python will show your technical fit 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 your approach to data modelling, validation, and ensuring data quality, as these are highlighted responsibilities.
Data Governance, Security, and Compliance
You should be able to discuss your understanding and experience with data governance and compliance, as these are important for the organisation.
What to research
, 4 itemsReview Advanced SQL and Python Techniques
Refresh your knowledge of advanced SQL queries, performance tuning, and Python scripting for data transformation.
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 past projects; if not, research Databricks basics and its role in modern data engineering.
Prepare Examples of Data Integration and Modelling
Gather concrete examples of integrating APIs, cloud sources, and developing robust data models.
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 handling and regulatory requirements.
What are the biggest data engineering challenges the team is currently facing?
Why ask this? To identify key priorities and areas where you can add immediate value.
How does the data engineering team collaborate with AI/ML specialists and DevOps?
Why ask this? To learn about cross-functional workflows and integration points.
What opportunities are there for learning and professional growth within the team?
Why ask this? To assess the organisation's commitment to your development and career progression.
How is success measured for data engineering projects in this organisation?
Why ask this? To understand performance expectations and how your contributions will be evaluated.
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