Data Engineer
Read straight from GWI's careers page, verified 1h ago.Seekless can tailor your résumé to it.
Seekless can write your résumé for this posting first.
Before you apply
LocationGreece, Athens, GR
Work modehybrid
Typefull-time
SeniorityMid
DepartmentTechnology
Company size501–1,000 people
PostedNov 3, 2026 · 3d ago
Verified live1h ago
At a glanceSummarised by Seekless from the posting.
Strong track record of impactful delivery as a Data or Analytics Engineer, with proficiency in SQL and Python
Hands-on experience with ingestion, orchestration, modelling, transformation and data quality on a cloud data stack, such as BigQuery, Airflow and dbt (or equivalents)
Engineering practices: Git, code reviews, testing, documentation and CI/CD, plus comfort using AI-assisted development tools while reviewing and testing their output with care
Knowledge of data accessibility, cataloguing and quality assurance
Ability to scope, estimate and deliver work independently in an Agile environment, and communicate well with analysts and stakeholders
Exposure to preparing data for AI use cases, such as semantic and metrics layers, well-documented models, and LLM-based tooling (RAG, embeddings, vector databases)
Skills
SQL
Python
BigQuery
Airflow
dbt
Git
CI/CD
Weekly office requirement: Hybrid, 1 day/week
Employment type: Permanent, full-time, employee
Linked Seniority level: Mid-Senior
About the company
Every business is using AI. The ones that get it right start with real people. GWI is the human intelligence underneath: verified data that helps businesses understand what moves their audience, and act on it faster. If you want your work to matter, keep reading.
What will I be doing?
About the role
As our Data Engineer you’ll maintain and enhance the Business Data Platform that sits at the heart of our BI Engineering team, serving analytics built on GWI’s internal data to the wider business so it can make confident decisions. You’ll work closely with colleagues across GWI to keep pipelines dependable, data models clear and analytics-ready, and data securely accessible across dashboards and AI tooling.
The accuracy and accessibility of our data shape how we measure performance, plan ahead and adopt AI-assisted analytics, so your work directly supports GWI’s goal of putting consumer insights at the heart of every organisation.
A few things you’ll be responsible for:
•
Building and running reliable, well-monitored data pipelines, infrastructure and warehouse, backed by strong testing and data quality practices.
•
Designing analytics-ready data models with clear lineage and documentation, in close partnership with analysts and stakeholders.
•
Optimising warehouse cost and performance through query tuning, efficient data modelling and careful resource management.
•
Managing data access so the right people and AI assistants can securely find, understand and use trusted, well-documented data.
Working at GWI means a high tempo environment where change is common. We’re building quickly because the industry is moving quickly, and our customers rely on us to stay ahead. If you like being part of that, you’ll enjoy it here.
What do I need to bring with me?
You’ll need to be able to demonstrate the core skills this role requires. You don’t have to tick all the boxes right away; the important thing is that you’re willing to learn.
•
Data engineering experience: a strong track record of impactful delivery as a Data or Analytics Engineer, with proficiency in SQL and Python.
•
End-to-end pipelines: hands-on experience with ingestion, orchestration, modelling, transformation and data quality on a cloud data stack, such as BigQuery, Airflow and dbt (or equivalents).
•
Engineering practices: Git, code reviews, testing, documentation and CI/CD, plus comfort using AI-assisted development tools while reviewing and testing their output with care.
•
Data governance: knowledge of data accessibility, cataloguing and quality assurance.
•
Independence and collaboration: you can scope, estimate and deliver work independently in an Agile environment, and you communicate well with analysts and stakeholders.
•
A plus: exposure to preparing data for AI use cases, such as semantic and metrics layers, well-documented models, and LLM-based tooling (RAG, embeddings, vector databases).