Data Engineer

We're looking for a fast-learner, early to mid-career Data Engineer to join our growing London team and help power our real estate market intelligence platform. Working alongside our Data team in our London office, you'll design and maintain the ETL pipelines, scrapers, and transformation workflows that capture millions of data points across the UK and Europe daily.

This is a hands-on engineering role: you'll be building pipelines, shipping production code, and shaping how we work with data, not just maintaining what's already there.

We operate a hybrid working model, with team members typically in our London office around 3 days a week. Exact arrangements may vary by team and manager. We're open to considering visa sponsorship for the right candidate., · Design, build, and maintain efficient and reliable data pipelines using Python and GCP, supporting daily ingestion of rental and availability data across multiple geographies.

· Develop and maintain Python-based web scrapers (Playwright, BS4).

· Write and optimise SQL transformation workflows (Dataform, BigQuery) to turn raw scraper output into clean, analytics-ready datasets.

· Build and maintain LLM-driven workflows within our ETL pipelines, including sensible checks for output quality, hallucination, and graceful failure.

· Implement data quality and validation frameworks across our extraction and transformation layers to ensure integrity at scale.

· Collaborate closely with the wider Data team and cross-functional stakeholders to support data-driven decision-making across the business.

· Stay current with developments and best practices in data engineering and bring them into how we work.

Do you have experience in SQL?, Do you have a Master's degree?, · Master's degree in Computer Science, Engineering, Data Science, or a related field.

· 3+ years of professional experience as a Data Engineer or in a closely related role.

· Strong Python skills, including object-oriented programming and building production ETL/ELT pipelines. 3+ years of professional, hands-on experience is ideal.

· Strong SQL skills, able to write and optimise advanced queries for transformation and analytics. 3+ years of professional, hands-on experience is ideal.

· Hands-on experience with cloud data platforms. GCP (BigQuery, Cloud Storage, Cloud Run, Firestore, Dataform) is preferred, but equivalent experience on AWS or Azure is welcome. We care more about depth than the specific provider.

· Experience working with web scraping libraries (Playwright, Scrapy, or similar).

· Working knowledge of LLM APIs (OpenAI, Gemini, or similar) and how to integrate them into data pipelines.

· Familiarity with Git, Linux, Docker, and CI/CD workflows (GitHub Actions or similar).

· Experience with NoSQL datastores (Firestore or similar).

· Strong problem-solving instincts and good written and verbal communication.

· Experience with data testing frameworks (Pytest, Great Expectations, Dataplex).

Preferred Additional Skills

· Hands-on experience with AI-assisted IDEs and LLM-powered coding tools is desired.

· Experience with Terraform or other IaC tools.

· Experience with data visualisation tools (Tableau, Power BI, Looker).

· Familiarity with machine learning or data science concepts.

· Certified GCP Data Engineer.

· Experience working in a fast-paced, agile environment.

Technical Stack

You'll be working within (and contributing to) a stack that includes:

· Languages & libraries: Python, SQL, YAML

· GCP: BigQuery, Cloud Storage, Cloud Run Jobs/Functions, Cloud Build, Cloud Scheduler, Firestore, Dataform, Artifact Registry, Secret Manager, Dataflow, Compute Engine

· DevOps & tooling: GitHub, GitHub Actions, Docker, Terraform, Linux, Pytest, Pydantic, Jira

Amro Data Labs is a specialist intelligence, AI and automation consultancy for the real estate investment and asset management sector. Our flagship client, Amro Partners, is a high-growth European real estate investment firm focused on the Living Sector. Our team works at the intersection of data science, AI/ML, engineering, and automation to deliver institutional-grade decision infrastructure.