Data Engineer
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid
- Salary
- €75k-85k/year (EUR)
- Category
- Data engineering
Technologies & skills
What you'll be doing
Join a Dublin-based data team as a Data Engineer to build and scale a modern data stack. Collaborate with analysts, data scientists, and product teams to transform raw data into reliable datasets. The role emphasizes clean engineering, ownership, and continuous improvement in a hands-on environment.
- Design, build and maintain batch and streaming data pipelines on AWS
- Model and transform data in Snowflake using dbt, following good testing and documentation practices
- Orchestrate workflows with Airflow (or similar) and monitor data quality and pipeline performance
- Work with stakeholders to understand data needs and deliver datasets ready for analysis
- Help improve CI/CD, version control and engineering standards across the team
Key requirements
Must-have
- Around 3+ years' experience in a data engineering role
- Strong SQL and Python
- Hands-on experience with Snowflake and dbt
- Experience with AWS data services (e.g. S3, Glue, Lambda, Redshift or similar)
- Familiarity with orchestration tools such as Airflow
- Familiarity with Git-based workflows
- Clear communication and a collaborative approach
Nice-to-have
- Kafka or Kinesis
- Terraform
- Data observability tooling
- Experience in a regulated industry
Experience: 3+ years in a data engineering role
Role signals
- Technical focus
- data engineering
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
**Data Engineer **
Our client, a Dublin-based business, has a growing data team and is looking for a Data Engineer to build and scale its modern data stack. You'll work closely with analysts, data scientists and product teams to turn raw data into reliable, well-modelled datasets that people across the business use every day. It's a hands-on role in a team that values clean engineering, ownership and continuous improvement.
What you'll do
-
Design, build and maintain batch and streaming data pipelines on AWS
-
Model and transform data in Snowflake using dbt, following good testing and documentation practices
-
Orchestrate workflows with Airflow (or similar) and monitor data quality and pipeline performance
-
Work with stakeholders to understand data needs and deliver datasets ready for analysis
-
Help improve CI/CD, version control and engineering standards across the team
What you'll bring -
Around 3+ years' experience in a data engineering role
-
Strong SQL and Python
-
Hands-on experience with Snowflake and dbt
-
Experience with AWS data services (e.g. S3, Glue, Lambda, Redshift or similar)
-
Familiarity with orchestration tools such as Airflow and with Git-based workflows
-
Clear communication and a collaborative approach
Nice to have: Kafka or Kinesis, Terraform, data observability tooling, or experience in a regulated industry.
What's on offer
- Competitive salary
- Hybrid working
- Good training and certification budget
**If you are interested, apply via the link below! **
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 Data Engineer role focuses on building and maintaining modern data pipelines using AWS, Snowflake, dbt, and Airflow, while collaborating closely with analysts, data scientists, and product teams. The position emphasizes hands-on engineering, clean code, and continuous improvement within a hybrid Dublin-based team.
- Hands-on data engineering with modern stack·High
- Collaboration and communication·High
- Continuous improvement and clean engineering·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Key Technologies
0–10 minRefresh your knowledge and prepare examples involving AWS data services, Snowflake, dbt, Airflow, Python, and SQL.
Prepare STAR Stories
10–17 minDevelop concise STAR (Situation, Task, Action, Result) examples for pipeline design, data modeling, workflow orchestration, and stakeholder collaboration.
Research the Team and Company
17–22 minLook up the company's data team structure, recent projects, and any public information about their data stack or culture.
Draft Role-Specific Questions
22–26 minPrepare thoughtful questions about team structure, technical challenges, and engineering practices to ask during the interview.
Practice Technical Explanations
26–30 minRehearse explaining technical concepts (e.g., Airflow DAGs, dbt models) clearly and concisely for both technical and non-technical audiences.
Talking points
, 5 itemsBuilding Data Pipelines on AWS
Demonstrate your experience designing, implementing, and maintaining scalable batch and streaming pipelines using AWS services such as S3, Glue, Lambda, and Redshift.
Data Modeling and Transformation with Snowflake and dbt
Showcase your ability to model, transform, and document data in Snowflake using dbt, including testing and best practices.
Workflow Orchestration with Airflow
Explain how you have used Airflow (or similar tools) to schedule, monitor, and manage data workflows, ensuring reliability and data quality.
CI/CD and Engineering Standards
Provide examples of how you have contributed to improving CI/CD pipelines, version control practices, and overall engineering standards in previous teams.
Stakeholder Collaboration
Describe situations where you worked with analysts, data scientists, or product teams to understand data needs and deliver actionable datasets.
What to research
, 4 itemsAWS Data Services
Review your experience with AWS services such as S3, Glue, Lambda, and Redshift, focusing on how you have used them in data pipelines.
Snowflake and dbt
Prepare examples of data modeling, transformation, and documentation using Snowflake and dbt, including testing strategies.
Airflow and Workflow Orchestration
Refresh your knowledge of Airflow concepts, DAG design, scheduling, and monitoring, and be ready to discuss real-world use cases.
CI/CD and Version Control
Be ready to discuss your experience with Git-based workflows, CI/CD pipelines, and how you have contributed to engineering standards.
Questions to ask
, 6 itemsHow is the data engineering team structured, and how does it interact with analysts and data scientists?
Why ask this? Clarifies collaboration expectations and team dynamics.
What are the main data sources and types of data pipelines currently in use?
Why ask this? Helps you understand the technical landscape and challenges.
How does the team approach data quality, testing, and observability?
Why ask this? Reveals the maturity of engineering practices and expectations.
What are the biggest challenges the data team is currently facing?
Why ask this? Gives insight into immediate priorities and where you can add value.
How is success measured for this role and the data engineering team?
Why ask this? Clarifies performance expectations and growth opportunities.
Are there opportunities to contribute to infrastructure automation or tooling improvements?
Why ask this? Shows your interest in continuous improvement and technical ownership.
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