At a Glance
- Tasks: Design and develop data quality checks and improve web-scraping infrastructure.
- Company: High-growth UK PropTech business with a focus on data accuracy.
- Benefits: 37 days' leave, hybrid work, learning budget, and private healthcare.
- Other info: Exciting opportunities for career growth and collaboration with innovative teams.
- Why this job: Join a dynamic team solving complex data challenges and making an impact.
- Qualifications: Strong Python and SQL skills, with experience in data quality processes.
The predicted salary is between 72000 - 88000 £ per year.
A high-growth UK PropTech business is looking for a Senior Data QA Engineer to take ownership of the accuracy, freshness and reliability of the data powering its platform. The business has built a large-scale property data platform, integrating thousands of data sources and processing terabytes of information. This role will focus on strengthening data quality, improving scraping resilience and building practical tools to identify and resolve issues before they reach customers.
The Role
- Design and develop data quality checks covering validation, anomaly detection, freshness and coverage.
- Own and improve production web-scraping infrastructure, including Playwright, proxies and anti-bot handling.
- Build and maintain data orchestration workflows using Dagster or similar.
- Develop internal QA and investigation tools to improve how data issues are identified and resolved.
- Use Python, SQL, dbt and BigQuery to diagnose root causes and improve data reliability.
- Work with TypeScript/Node.js where required.
- Partner with data and engineering teams to continuously raise the quality bar.
- Use AI-assisted development tools to build and ship solutions efficiently.
About You
- Strong Python and SQL skills, with hands-on dbt and BigQuery experience.
- Proven experience designing data quality, validation and anomaly detection processes.
- Production experience with web scraping at scale, ideally using Playwright or an equivalent.
- Experience with big data sets and big data warehouses.
- Experience with proxies, anti-bot measures and scraper monitoring.
- Experience with Dagster, Airflow, Prefect or another data orchestrator.
- Comfort working with TypeScript/Node.js.
- A pragmatic, hands-on approach and strong problem-solving skills.
Desirable:
- Geospatial data experience (PostGIS, GeoPandas, GDAL), GCP/Kubernetes/Docker experience.
- Knowledge of the UK property/planning sector.
Benefits
- 37 days' annual leave, including birthday and Christmas shutdown.
- Hybrid working and home-working equipment.
- £500 annual learning and development budget.
- Private healthcare.
- Salary-sacrifice pension scheme.
- Regular team events and monthly team lunches.
If you're a senior data professional who enjoys solving complex data quality problems and building robust systems at scale, please apply for more information.
Senior Data Quality Engineer employer: develop
As a Central Government Sector Leader at our client, you will join an innovative AI-native consultancy that prioritises employee growth and offers a dynamic work culture. With a competitive salary and substantial bonus potential, you will have the opportunity to shape the future of public sector consulting while enjoying a flexible work environment in Farringdon, fostering collaboration and creativity among talented professionals dedicated to making a meaningful impact.
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We think this is how you could land Senior Data Quality Engineer
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We think you need these skills to ace Senior Data Quality Engineer
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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How to prepare for a job interview at develop
✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
