Lead Data Engineer

Reperio Human Capital · Recruitment agency
Full-time•Data engineering•€500-550/day (EUR)•Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Full-time
Workplace
Hybrid
Day rate
€500-550/day (EUR)

Technologies & skills

Cloud & infrastructure

GCP

Other technical skills

BigQuerySQL

What you'll be doing

Lead Data Engineer (hybrid, Dublin, Ireland) to join a large data and analytics team for a major cloud data transformation. Responsibilities include designing, building, and supporting data pipelines and BI solutions, migrating legacy analytics workloads to BigQuery, and using AI-assisted development tools.

  • Build and improve BI and analytics solutions to agreed technical standards
  • Write SQL and scripts to manage data and ETL processes
  • Help migrate legacy analytics workloads to BigQuery
  • Use AI-assisted development tools as part of workflow

Key requirements

Must-have

  • Proficiency in SQL
  • Hands-on experience with relational databases, including legacy platforms
  • Experience with big data or NoSQL technologies such as Hadoop
  • Experience building and maintaining ETL processes
  • Comfortable working with structured data formats such as XML, JSON, and flat files

Nice-to-have

  • Experience with Google Cloud Platform, particularly BigQuery
  • Previous involvement in an on-premise to cloud migration
  • Experience using AI-assisted coding tools

Role signals

Technical focus
data engineering
Hands-on vs management
Hands-on

Full job description

I'm currently recruiting for a Data Engineer based in Dublin, focusing on hybrid working. This is an initial 6-month contract, strong day rate available. This is a chance to work on a major cloud data transformation within a large, complex organisation. You'll join an established data and analytics team and help design, build and support data pipelines and BI solutions.

About the Role

  • Build and improve BI and analytics solutions to agreed technical standards

  • Write SQL and scripts to manage data and ETL processes

  • Help migrate legacy analytics workloads to BigQuery

  • Use AI-assisted development tools as part of your workflow

Requirements

  • Proficiency in SQL and hands-on experience with relational databases, including legacy platforms

  • Experience with big data or NoSQL technologies such as Hadoop

  • Experience building and maintaining ETL processes

  • Comfortable working with structured data formats such as XML, JSON and flat files

Desirable

  • Experience with Google Cloud Platform, particularly BigQuery

  • Previous involvement in an on-premise to cloud migration

  • Experience using AI-assisted coding tools

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 Lead Data Engineer role focuses on designing, building, and supporting data pipelines and BI solutions, with a strong emphasis on SQL, ETL processes, and cloud migration to BigQuery within a large organization.

  • Hands-on technical expertise·High
  • Cloud migration experience·High
  • ETL and data pipeline skills·High

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review Recent Cloud Migration Projects

    0–8 min

    Prepare a concise summary of your experience migrating analytics workloads to cloud platforms, focusing on BigQuery if possible.

  2. Practice Explaining ETL Pipeline Design

    8–15 min

    Outline your approach to building and maintaining ETL processes, including tools, challenges, and solutions.

  3. Refresh Advanced SQL Skills

    15–22 min

    Work through examples of complex queries and performance tuning, especially in large-scale or legacy environments.

  4. Prepare Examples of Handling Structured Data

    22–27 min

    Recall specific instances where you worked with XML, JSON, or flat files, focusing on validation and transformation.

  5. Research AI-Assisted Development Tools

    27–30 min

    Familiarize yourself with current AI tools for data engineering and prepare to discuss their impact on your workflow.

Likely questions

, 6 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 5 items

  • SQL Proficiency and Legacy Database Experience

    Demonstrate your ability to write complex queries and manage data across both modern and legacy relational platforms.

  • ETL Pipeline Design and Maintenance

    Showcase your experience building robust, scalable ETL processes and troubleshooting data integration issues.

  • Cloud Migration Projects (BigQuery, GCP)

    Highlight your involvement in migrating analytics workloads from on-premise to cloud environments, especially BigQuery.

  • Handling Structured Data Formats (XML, JSON, Flat Files)

    Provide examples of ingesting, transforming, and managing data in various structured formats as part of data engineering workflows.

  • Use of AI-Assisted Development Tools

    Discuss how you have leveraged AI tools to improve coding efficiency or data engineering tasks.

What to research

, 4 items

  • Review BigQuery and GCP Data Migration Best Practices

    Study the process and common pitfalls of migrating analytics workloads from on-premise systems to BigQuery on GCP.

  • Brush Up on Advanced SQL and Query Optimization

    Prepare examples of complex SQL queries and performance tuning in both legacy and cloud environments.

  • Refresh Knowledge of ETL Tools and Data Pipeline Design

    Be ready to discuss your approach to building, maintaining, and troubleshooting ETL pipelines for large datasets.

  • Practice Working with Structured Data Formats

    Review handling, validation, and transformation of XML, JSON, and flat files in data engineering workflows.

Questions to ask

, 6 items
  1. What are the main challenges the team is currently facing with the cloud data transformation project?

    Why ask this? To understand the project's complexity and where your skills can add value.

  2. How is success measured for this role during the initial 6-month contract?

    Why ask this? To clarify expectations and key deliverables.

  3. What tools and frameworks are currently used for ETL and data pipeline orchestration?

    Why ask this? To assess the technical environment and alignment with your experience.

  4. How does the team leverage AI-assisted development tools in their workflow?

    Why ask this? To gauge the maturity and adoption of AI tools in the team's processes.

  5. What opportunities exist for contributing to the design or improvement of BI and analytics solutions?

    Why ask this? To understand the scope for technical input and innovation.

  6. How is collaboration managed between data engineers and other teams during migration projects?

    Why ask this? To learn about cross-functional communication and teamwork expectations.

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Apply NowApply before: 5 Nov 2026