Staff Data Engineer
TechJobs.ie Job Insights
At a glance
- Employment type
- Full-time
- Location
- Dublin, Ireland
- Workplace
- On-site
- Salary
- €130k-140k/year (EUR)
- Category
- Data engineering
Technologies & skills
What you'll be doing
Staff Data Engineer role in Dublin, Ireland, focused on building and scaling a high-throughput data platform handling petabytes of data and billions of events. Involves greenfield work, shaping architecture, and collaborating across teams in an engineering-led environment.
- Shape the data platform architecture
- Build high-throughput, reliable pipelines
- Work across teams including security, product, and billing
- Solve system-level problems
- Raise the bar for how data is used across the platform
Key requirements
Must-have
- Strong data engineering experience at scale
- Experience with distributed and event-driven systems
- AWS and modern tooling such as Kafka, Kinesis, and Postgres
- Strong coding skills in Python and SQL
- A systems mindset
Nice-to-have
- A strong interest in how data is actually used
- Comfort working without a playbook or perfect specs
Role signals
- Technical focus
- data engineering, distributed systems, platform architecture
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Overview
This is a Staff Data Engineer opportunity focused on building the data layer behind how modern software is stored, secured, and delivered at serious scale.
The platform handles tens of millions of requests daily and petabytes of data monthly, with data feeding directly into the product rather than sitting behind it.
Current areas of focus include:
- Building CDC pipelines so every change is tracked and nothing is lost
- Handling billions of events in near real-time
- Redesigning log and export systems at scale
- Laying the foundations for AI-driven insights
This role combines greenfield platform work with scaling existing systems.
What You'll Be Doing
- Shape the data platform architecture
- Build high-throughput, reliable pipelines
- Work across teams including security, product, and billing
- Solve system-level problems, not just tickets
- Raise the bar for how data is used across the platform
What You'll Need to Succeed
-
Strong data engineering experience at scale
-
Experience with distributed and event-driven systems
-
AWS and modern tooling such as Kafka, Kinesis, and Postgres
-
Strong coding skills in Python and SQL
Just as importantly, you'll bring: -
A strong interest in how data is actually used
-
A systems mindset, not just a pipeline mindset
-
Comfort working without a playbook or perfect specs
What's In It for You
- Core product infrastructure work, not reporting
- Real scale and real complexity
- Plenty of greenfield opportunity
- A data platform that is central to what gets built next
- Small team, high ownership, engineering-led environment
- Competitive salary plus stock in a recently funded, high-growth environment
Additional Information
Candidates must be based in Ireland or the UK and have the right to work. No sponsorship is available.
Location: Dublin - City Centre
Salary: EUR 130000 - 140000 / Year
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Staff Data Engineer role centers on architecting and scaling high-throughput, reliable data pipelines and platforms that directly power core product features at significant scale. The position emphasizes hands-on engineering, distributed systems, and cross-team collaboration in a high-ownership, engineering-led environment.
- Distributed Systems Expertise·High
- Hands-on Data Engineering·High
- Systems Thinking·High
- Cross-team Collaboration·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review and Summarize Relevant Projects
0–8 minList and outline your most relevant experiences with distributed data systems, CDC, and high-throughput pipelines, focusing on scale and impact.
Brush Up on Core Technologies
8–15 minRevisit key concepts and recent developments in AWS, Kafka, PostgreSQL, Python, and SQL, ensuring you can discuss hands-on usage.
Prepare STAR Stories for Collaboration and Problem Solving
15–21 minDraft concise stories highlighting cross-team work, system-level problem solving, and greenfield project leadership.
Research the Company's Product and Data Platform
21–26 minGather publicly available information about the company's platform, focusing on scale, architecture, and recent initiatives.
Draft Targeted Questions for the Interviewers
26–30 minSelect and tailor questions to clarify role expectations, team culture, and technical challenges.
Talking points
, 5 itemsDesigning and Scaling Distributed Data Pipelines
Demonstrate your experience architecting and optimizing pipelines that handle billions of events and petabytes of data, as required by the platform's scale.
Implementing Change Data Capture (CDC)
Showcase your ability to build systems that track every change and ensure data integrity, aligning with the focus on CDC pipelines.
Leveraging AWS, Kafka, and Modern Data Tooling
Highlight hands-on expertise with the core technologies listed, as these are essential for building and maintaining the platform.
Systems Thinking and Problem Solving
Illustrate your approach to solving system-level challenges and raising the bar for data usage, reflecting the need for a systems mindset.
Collaborating Across Teams
Prepare examples of working with security, product, and billing teams to deliver robust data solutions, as cross-team work is emphasized.
What to research
, 4 itemsReview Distributed Systems and Event-Driven Architectures
Refresh your knowledge of distributed data processing, event streaming, and real-time pipeline design, especially using Kafka and AWS.
Deep Dive into Change Data Capture (CDC) Patterns
Study CDC concepts, tools, and best practices, focusing on reliability and data integrity at scale.
Hands-on with AWS, Kafka, and PostgreSQL
Prepare concrete examples of using these technologies in production, including challenges faced and solutions implemented.
Cross-Team Communication Strategies
Think through examples where you worked with product, security, or billing teams to deliver data solutions.
Questions to ask
, 6 itemsWhat are the biggest technical challenges currently facing the data platform?
Why ask this? To understand the immediate priorities and where your expertise can have the most impact.
How does the team approach balancing greenfield projects with scaling existing systems?
Why ask this? To gauge how new initiatives are prioritized alongside ongoing operations.
What is the typical collaboration model between data engineering and other teams like security or product?
Why ask this? To clarify expectations for cross-functional work and communication.
How is success measured for this role and the data platform team?
Why ask this? To align your contributions with the team's goals and performance metrics.
What opportunities exist to influence the platform's technical direction?
Why ask this? To assess the level of ownership and architectural input expected from this role.
What are the most important qualities you look for in someone joining this small, high-ownership team?
Why ask this? To understand the team culture and expectations beyond technical skills.
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