Cloud Data Engineer (GCP) – Mid Level Greece
Satalia is hiring a Cloud Data Engineer (GCP) – Mid Level Greece for a remote role open to applicants in Greece. Level rates it ; you can apply on Satalia.
Level's read on this job
- On Level's scale, this data engineer role is Little AI ●○○○: aI is not part of the work.
- The posting lists this role as remote.
- Tools and skills the posting cites include Pinecone, Weaviate and pgvector.
- Level classifies it as a mid-level role.
- Level lists 11 other open roles at Satalia.
AI in this role
As an organisation, we push the boundaries of data science, optimisation and artificial intelligence to solve the most complex problems in the industry. Satalia, a WPP company is a community of individuals devoted to working on diverse and challenging projects, allowing you to flex your technical skills whilst working with a tight-knit team of high performing colleagues.
Led by our founder and WPP Chief AI Officer Daniel Hulme, Satalia’s ambition is to become a decentralised organisation of the future. Today, this involves developing tools and processes to liberate and automate manual repetitive tasks, with a focus on freedom, transparency and trust. At the core of our thinking is an approach to wellbeing and inclusivity. We unpack human behaviour and unpick prejudice to ensure a safe and inviting environment. We offer truly flexible working and allow our employees to find the working practice that makes them most productive. At Satalia, your opinion matters and your achievements are celebrated.
The Role:
You'll build and own the pipelines and data models that turn raw data into clean, reliable, well-documented datasets on GCP: the foundation for the AI agents and LLM-powered systems we run in production, as well as for our data scientists and analysts.
Our current work includes:
• Building data pipelines — robust ETL/ELT pipelines that ingest, transform, and serve large datasets across our cloud-native marketing intelligence stack..
• Data modelling and transformation — designing well-documented, tested, version- controlled data models (with dbt) that power downstream AI and BI.
• Warehouse and storage design — modelling data in our warehouse/lakehouse and relational databases, choosing the right structure for each access pattern.
• Data for AI agents: metadata, catalogues, and embeddings that let agents discover and query data safely.
• Trustworthy data — orchestration, monitoring, data quality checks, and lineage so the data people depend on is correct and dependable.
You'll work closely with data scientists and software engineers, own your pipelines end to end, and help shape how we build data at Satalia.
What you'll be doing:
• Design, build, and maintain production data pipelines that deliver clean, reliable data to analytics, BI, and AI workflows.
• Build SQL-based data transformations and models into well-documented, tested datasets that others can trust and reuse.
• Design and operate our data warehouse/lakehouse for the way the data is actually used.
• Build data-quality, monitoring, and lineage into pipelines so problems are caught early.
• Contribute to pipeline infrastructure — orchestration, CI/CD, and infrastructure-as- code — for the data platform.
• Share what you know with the rest of the team.
What we want from you:
• 4+ years building and running production data pipelines and data models.
• Strong SQL and data modelling(dbt or similar is a plus).
• Strong Python, with good software engineering habits — version control, testing, code review, CI/CD.
• Experience with pipeline orchestration (Airflow, Dagster, or similar).
• Production experience on GCP (BigQuery, Pub/Sub, IAM) or equivalent AWS services, including infrastructure-as-code (Terraform).
• Exposure to LLM or agentic systems (RAG, embeddings, tool-using agents) and the data they depend on.
• Clear communication — you can write a short design doc that's useful to both product managers and engineers.
Nice to have:
• Distributed data processing at scale (Apache Spark or similar).
• Containers (Docker).
• Experience with vector databases (Pinecone, Weaviate, pgvector) or graph databases (Neo4j, Neptune).
• Event-driven or streaming data (Pub/Sub, Kafka) and real-time pipelines.
What we Offer:
Benefits - healthcare
Remote working - café, bedroom, beach - wherever works;
Truly flexible working hours - school pick up, volunteering, gym;
Generous Leave – inline with Greek Labour Law
Impactful projects - focus on bringing meaningful social and environmental change;
People oriented culture - wellbeing is a priority, as is being a nice person;
Transparent and open culture - you will be heard;
Development - focus on bringing the best out of each other;
Satalia is home to some of the brightest minds in AI and if you’re looking to join a company who not only values autonomy and freedom, but embraces a culture of inclusion and warmth, we’d love to hear from you.
We aim to respond to all applications within 2 weeks. If you have not heard from us within 2 weeks this means your application has been unsuccessful.
By applying to Satalia you are expressly giving your consent for the collection and use of your information as described within our Satalia Recruitment Privacy Policy.
Good luck!
How we rate this
Cloud Data Engineer (GCP) – Mid Level Greece at Satalia rates 9 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data engineer jobs at this level
Pay
- Typical data engineer job
- $147k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- What are the limits of Pinecone that you've run into, and how did you work around them?
- What's a project where you used Weaviate hands-on?
- Walk me through how you've used pgvector in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Pinecone, Weaviate, and pgvector. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
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