Senior Data Engineer
TechJobs.ie Job Insights
At a glance
- Employment type
- Contract
- Workplace
- Hybrid
- Day rate
- €400-500/day (EUR)
- Category
- Data engineering
What you'll be doing
Lead Data Engineer (12-month contract, hybrid in Ireland) responsible for tracking, modeling, and ensuring quality of customer behavior and product data. Collaborate with product, engineering, and analytics teams to turn user activity into trusted data. Requires hands-on experience with behavioral/telemetry data.
- Lead tracking, modeling, and quality of customer behavior and product data
- Turn user activity into trusted data reflecting product usage
- Collaborate with product, engineering, and analytics teams
Key requirements
Must-have
- Working knowledge of Git
- Experience with CI/CD
- Experience with automated testing
- Previous data engineering experience
- Hands-on experience with behavioral or telemetry data in production using an event collection platform
- Proficiency with SQL
- Proficiency with Python
Nice-to-have
- Understanding of RUM and observability (metrics, logs, traces)
- Experience with data privacy, classification, and lifecycle practices for behavioral data
- Strong communication skills for explaining complex event data
Experience: Previous experience in a data engineering role
Role signals
- Technical focus
- data engineering, behavioral/telemetry data
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
I'm currently recruiting for a Lead Data Engineer based in Dublin, this is an initial 12-month contract. Focusing on hybrid working, strong day rate available.
About the role
-
Lead the tracking, modelling and quality of customer behaviour and product data
-
Turn user activity into trusted data that shows how customers use the product
-
Work closely with product, engineering and analytics teams
Requirements
-
Working knowledge of Git, CI/CD and automated testing
-
Previous experience in a data engineering role
-
Strong hands-on experience with behavioural or telemetry data in production, using an event collection platform such as Snowplow, Adobe Experience Platform or Google Analytics.
-
Proficiency with SQL and Python.
Desirable
-
Understanding of RUM and observability (metrics, logs, traces)
-
Experience with data privacy, classification and lifecycle practices for behavioural data.
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Strong communication skills: you can explain complex event data to technical and non-technical audiences.
If this role sounds of interest to you then apply through the link provided 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 Senior Data Engineer contract role in Ireland focuses on leading the tracking, modeling, and quality of customer behavior and product data, with a strong emphasis on hands-on experience with behavioral or telemetry data, Python, SQL, and modern data engineering practices.
- Hands-on experience with behavioral/telemetry data·High
- Proficiency with Python and SQL·High
- CI/CD and automated testing in data pipelines·Medium
- Cross-functional communication·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Behavioral Data Projects
0–8 minPrepare concise examples of your work with event collection platforms and behavioral data, focusing on your role and impact.
Brush Up on CI/CD and Testing Practices
8–14 minRevisit your experience with Git, CI/CD pipelines, and automated testing in data engineering contexts.
Refresh Python and SQL Skills
14–20 minReview recent projects or code samples that demonstrate your proficiency in building and optimizing data pipelines.
Prepare for Cross-Functional Collaboration Examples
20–25 minThink of situations where you worked closely with product, engineering, or analytics teams and the outcomes achieved.
Research Data Privacy Practices
25–30 minUpdate yourself on current best practices for data privacy and lifecycle management, especially as they relate to behavioral data.
Talking points
, 5 itemsBehavioral and Telemetry Data in Production
Demonstrate your experience working with event collection platforms (e.g., Snowplow, Adobe Experience Platform, Google Analytics) and how you have turned raw user activity into actionable, trusted data.
Data Pipeline Development with Python and SQL
Showcase your ability to build, maintain, and optimize data pipelines using Python and SQL, especially for large-scale behavioral datasets.
CI/CD, Git, and Automated Testing for Data Engineering
Highlight your familiarity with modern development workflows, including version control, continuous integration/deployment, and automated testing in the context of data engineering.
Collaboration with Product, Engineering, and Analytics Teams
Prepare examples of how you have worked closely with cross-functional teams to ensure data quality and deliver insights that drive product decisions.
Data Privacy and Lifecycle Management
If applicable, discuss your understanding or experience with data privacy, classification, and lifecycle practices, especially for behavioral data.
What to research
, 4 itemsEvent Collection Platforms
Review your experience with platforms like Snowplow, Adobe Experience Platform, or Google Analytics, focusing on how you have used them in production.
CI/CD and Automated Testing in Data Engineering
Refresh your knowledge of implementing CI/CD pipelines and automated testing for data workflows using Git and related tools.
Python and SQL for Data Pipelines
Prepare to discuss and demonstrate your proficiency in building and optimizing data pipelines with Python and SQL.
Data Privacy and Lifecycle Practices
Be ready to talk about your understanding or experience with data privacy, classification, and lifecycle management, especially for behavioral data.
Questions to ask
, 6 itemsWhat event collection platforms and data infrastructure are currently in use?
Why ask this? Clarifies the technical environment and helps you tailor your examples.
How does the team approach data privacy and lifecycle management for behavioral data?
Why ask this? Shows your interest in compliance and best practices.
What are the main challenges the team faces with behavioral or telemetry data today?
Why ask this? Helps you understand the team's pain points and where you can add value.
How is collaboration structured between data engineering, product, and analytics teams?
Why ask this? Gives insight into cross-functional workflows and expectations.
What does success look like for this role over the next 12 months?
Why ask this? Clarifies expectations and key deliverables for the contract period.
Are there opportunities to influence data architecture or introduce new tools and practices?
Why ask this? Assesses the scope for innovation and technical leadership.
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