Lead Data Engineer
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At a glance
- Employment type
- Contract
- Workplace
- Hybrid
- Day rate
- €500-550/day (EUR)
- Category
- Data engineering
Technologies & skills
What you'll be doing
Lead Data Engineer (6-month contract, hybrid in Dublin) to own technical strategy and delivery for a data and analytics engineering function on Microsoft Fabric. Lead a team, set architecture, standards, and roadmap, covering data ingestion to modelling. Collaborate with BI, Reporting, and Data Governance.
- Set technical strategy and architecture across ingestion, transformation, and semantic layers
- Own platform reliability, scalability, and cost; prioritize investment against business demand and risk
- Set data modelling and documentation standards in the data catalogue
- Collaborate with BI & Reporting and Data Governance to ensure well-governed data pipelines
Key requirements
Must-have
- Previous experience as a Senior or Lead Data Engineer
- Strong hands-on data modelling expertise
- Proficiency with SQL
- Proficiency with Python
- Technical experience with Microsoft Fabric, Azure Synapse, Databricks, and Snowflake
- Experience setting technical architecture and standards for pipeline engineering and data modelling
Nice-to-have
- Experience in energy, utilities, or another regulated industry
- Exposure to data governance and cataloguing tools
- Experience setting up data testing and CI/CD practices
Role signals
- Technical focus
- data engineering
- Leadership
- People management
- Architecture / system design
- Indicated in the listing
- 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 6-month contract, focusing on hybrid working. Strong day rate available.
Our client is hiring a Data Engineering Lead to own the technical strategy and delivery of its combined data and analytics engineering function. You'll lead a team of Data Engineers and set the architecture, standards and roadmap on a Microsoft Fabric platform, covering everything from data ingestion to modelling.
About the Role
- Set the technical strategy and architecture across ingestion, transformation and semantic layers
- Own the platform's reliability, scalability and cost, and prioritise investment against business demand and risk
- Set data modelling and documentation standards in the data catalogue, so the team depends less on key people, and the business can use self-service analytics
- Work with BI & Reporting and Data Governance to keep the pipeline well-governed from raw data through to certified reporting datasets
Requirements
- Previous experience as a Senior or Lead Data Engineer with strong hands-on data modelling expertise
- Proficiency with SQL and Python skills
- Previous technical experience with Microsoft Fabric, Azure Synapse, Databricks and Snowflake
- Experience setting technical architecture and standards for both pipeline engineering and data modelling
Desirable:
- Experience in energy, utilities or another regulated industry
- Exposure to data governance and cataloguing tools
- Experience setting up data testing and CI/CD practices
If this role sounds of interest to you, then apply through the link provided
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Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Lead Data Engineer role is a hybrid contract position in Dublin, responsible for setting technical strategy, architecture, and standards for a Microsoft Fabric-based data platform, while leading a team and ensuring robust data engineering practices.
- Technical Leadership·High
- Hands-on Data Engineering·High
- Platform Architecture·High
- Collaboration and Governance·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Microsoft Fabric and Related Technologies
0–8 minSpend time revisiting the architecture and integration points of Microsoft Fabric, Azure Synapse, Databricks, and Snowflake.
Prepare Data Modelling and Documentation Examples
8–14 minSelect 1-2 strong examples from your experience where you set or improved data modelling and documentation standards.
Refresh CI/CD and Data Testing Knowledge
14–20 minReview best practices and tools for implementing CI/CD and automated data testing in data engineering pipelines.
Reflect on Leadership and Collaboration Stories
20–25 minIdentify specific instances where you led teams and collaborated with BI or governance functions to drive results.
Research the Client and Industry Context
25–30 minIf possible, learn about the client’s business, especially if they are in energy, utilities, or a regulated sector, to tailor your examples.
Talking points
, 5 itemsTechnical Strategy and Architecture
Be ready to discuss how you have set or influenced technical direction and architectural decisions for data platforms, especially across ingestion, transformation, and semantic layers.
Hands-on Data Modelling
Prepare examples of your expertise in designing and implementing robust data models, including standards and documentation practices.
Experience with Microsoft Fabric, Azure Synapse, Databricks, and Snowflake
Demonstrate your proficiency and practical experience with these platforms, highlighting relevant projects and technical challenges solved.
Pipeline Engineering Standards and CI/CD
Showcase your experience in establishing engineering standards, including data testing and CI/CD practices, to ensure reliable and scalable pipelines.
Collaboration with Data Governance and BI Teams
Share examples of working with cross-functional teams to ensure data pipelines are well-governed and support self-service analytics.
What to research
, 4 itemsMicrosoft Fabric Platform
Review the architecture, features, and best practices for Microsoft Fabric, as it is central to the platform.
Data Modelling Standards and Documentation
Prepare to discuss frameworks and tools for data modelling and cataloguing, including how to document and standardize models.
CI/CD and Data Testing in Data Engineering
Refresh your knowledge of CI/CD pipelines and automated data testing practices relevant to data engineering.
Collaboration with Data Governance and BI Teams
Think of examples where you worked with governance or analytics teams to improve data quality and pipeline governance.
Questions to ask
, 6 itemsWhat are the immediate technical challenges or priorities for the data platform over the next six months?
Why ask this? Clarifies expectations and helps you understand where your expertise will have the most impact.
How is the data engineering team currently structured, and what is the team's size and skill set?
Why ask this? Provides insight into your leadership responsibilities and the team's capabilities.
What tools and processes are currently in place for data governance and cataloguing?
Why ask this? Helps you gauge the maturity of data governance and where you can contribute improvements.
How does the organization measure the success of its data engineering function?
Why ask this? Reveals key performance indicators and business alignment for the role.
What is the relationship between the data engineering team and BI/Reporting teams?
Why ask this? Clarifies collaboration expectations and cross-team workflows.
Are there any upcoming initiatives involving new technologies or significant changes to the data platform?
Why ask this? Shows your interest in innovation and readiness for future challenges.
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