At a Glance
- Tasks: Design and scale data pipelines for AI governance and risk management.
- Company: Join a leading tech firm focused on innovative governance solutions.
- Benefits: Competitive salary, remote work options, and opportunities for professional growth.
- Other info: Dynamic role with exposure to large-scale systems and innovative data architecture.
- Why this job: Make a real impact in AI governance while working with cutting-edge technologies.
- Qualifications: 5+ years in Data Engineering, strong Python and SQL skills required.
The predicted salary is between 60000 - 80000 £ per year.
Qualitest is looking for a senior AI-Native Data Engineer to join the Governance Technology Team within our clients Research’s Risk, Resiliency & Regulatory Governance organisation. This team is focused on building the next generation of governance infrastructure to support AI governance, risk management, security, privacy, and regulatory oversight across Research and Labs product areas.
In this role, you will be responsible for designing and scaling robust data pipelines and governance graph architectures that enable structured oversight of datasets, models, systems, approvals, controls, and risks. You will work closely with cross-functional stakeholders including ML Engineers, Security Engineers, Privacy Engineers, TPMs, and Product teams to operationalise graph-based governance solutions across highly complex technical environments. The position sits at the intersection of large-scale data engineering, AI systems, graph technologies, and governance frameworks within a highly innovative and fast-paced environment.
Key Responsibilities- Design, build, and maintain scalable batch and streaming data pipelines using internal and cloud technologies
- Develop and evolve governance knowledge graphs representing datasets, models, approvals, systems, controls, and risk relationships
- Create unified data models to map relational and structured data into graph-based architectures
- Partner with governance stakeholders including security, privacy, legal, and risk teams to define graph ontology and schema requirements
- Establish and maintain data quality, integrity, lineage, and governance controls across pipelines and graph systems
- Support GraphRAG implementations using vector search, graph databases, and semantic search technologies
- Build and manage orchestration workflows using technologies such as Dataflow, Vertex AI, Cloud Composer (Airflow), and Plx Workflows
- Leverage AI-assisted development tooling and automated engineering workflows to improve engineering efficiency and delivery quality
- Contribute to CI/CD, automated testing, infrastructure as code, and software engineering best practices
- 5+ years of experience within Data Engineering, Backend Engineering, or Data Architecture environments
- Experience working with cloud data platforms, ideally within GCP environments including BigQuery, Dataflow, and Vertex AI
- Strong understanding of graph databases, graph data modelling, ontology design, and graph query languages such as GQL, Cypher, or SQL/PGQ
- Expert-level Python and SQL skills
- Strong understanding of data governance principles including data quality, metadata management, lineage, privacy, and security
- Experience working with modern SDLC practices including CI/CD, automated testing, and version control
- Experience using AI-assisted engineering and development tooling within engineering workflows
- Experience with Spanner Graph and/or BigQuery Graph
- Familiarity with vector databases and embedding generation pipelines
- Experience with multi-agent orchestration frameworks such as LangGraph
- Exposure to policy-as-code frameworks
- Experience with data cataloguing and automated lineage tooling
- Academic background in Computer Science, Data Science, Mathematics, or related technical disciplines
This is an opportunity to work on highly impactful governance and AI infrastructure initiatives supporting our clients Research and Labs environments. The role offers exposure to large-scale distributed systems, graph technologies, AI governance frameworks, and next-generation data architecture within a cutting-edge technical environment.
Data Engineer employer: Qualitest
Qualitest is an exceptional employer for Data Engineers, offering a dynamic work culture that fosters innovation and collaboration within the Governance Technology Team. Employees benefit from opportunities for professional growth through exposure to cutting-edge AI governance projects and large-scale data engineering challenges, all while working in a fast-paced environment that values creativity and technical excellence.
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We think this is how you could land Data Engineer
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We think you need these skills to ace Data Engineer
Some tips for your application 🫡
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Qualitest, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Qualitest. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Qualitest
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Qualitest!
✨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.
