At a Glance
- Tasks: Lead the development of robust data pipelines and optimise Azure Databricks for economic analysis.
- Company: Join a leading financial services firm with a focus on innovation and collaboration.
- Benefits: Competitive salary, hybrid work model, bonuses, and comprehensive benefits.
- Other info: Opportunity for mentorship and career growth in a dynamic environment.
- Why this job: Make a significant impact in economic data engineering while working with cutting-edge technology.
- Qualifications: 10+ years in data engineering, expertise in Azure Databricks, and strong coding skills.
- Senior Data Engineer – Monetary Analysis & Economic Data
- Location: London, UK (Hybrid: 3 days in office, 2 days remote)
- Security Clearance: SC Clearance Required (Active or eligible to undergo)
- Salary: £75,000 to £80,000 + Benefits and Bonus
- Position Type: Full-Time, Permanent
- Experience Level: 10+ Years (Senior/Lead)
About the Role
We are seeking an expert Hybrid Data Engineer to work in London 3 days in office, 2 days remote paying £75,000 to £80,000 + Benefits and Bonus to drive the development, optimization, and scaling of our cutting-edge Azure Databricks platform.
This high-performance infrastructure is critical to our core mission, directly powering our Monetary Analysis, Forecasting, and Modelling frameworks.
In this role, you will lead the engineering of robust, secure data pipelines, implement complex transformation logic, and guarantee absolute data reliability for business-critical economic datasets.
Key Responsibilities
- Data Pipeline Engineering & Processing
- Build & Scale: Design, develop, and maintain robust, scalable ETL/ELT pipelines ingesting data from APIs, relational databases, streaming services, and financial data providers.
- Complex Transformations: Implement advanced data processing logic for cleaning, enriching, and aggregating large-scale data using Spark (Py Spark/Scala) and SQL.
- Optimization: Fine-tune data workloads for maximum performance, throughput, and cloud cost-efficiency.
- Azure Databricks & Cloud Architecture
- Platform Ownership: Drive the implementation of Azure Databricks services, leveraging Unity Catalog and Delta Lake architectures.
- Polyglot Development: Develop and maintain data solutions using a diverse technical stack including Python, SQL, R, YAML, and Java Script.
- Data Quality, Governance & Security
- Data Governance: Lead hands-on implementation of Azure Purview to manage data quality, data governance, metadata, and end-to-end data lineage tracking.
- Framework Design: Establish automated data validation, quality checks, and real-time alerting processes across all production environments.
- Dev Ops, Automation & Collaboration
- CI/CD Integration: Partner with Dev Ops teams to design, build, and maintain robust CI/CD pipelines for automated environmental deployments.
- Cross-Functional Partnership: Collaborate closely with economists, data scientists, and senior analysts to translate complex analytical needs into production-ready data systems.
- Mentorship & Quality: Drive engineering excellence through active participation in code reviews, architectural discussions, and knowledge-sharing sessions.
- Technical Stack & Qualifications
Essential Experience
- Core Data Engineering: 10+ years of dedicated data engineering experience managing massive, complex datasets.
- Azure Databricks Expertise: 3+ years of deep, hands-on production experience with Azure Databricks, Spark (Py Spark/Scala), and Delta Lake ecosystems.
- Cloud Infrastructure: Extensive experience across the Azure data suite, specifically Azure Data Factory, Azure Blob Storage, and Azure SQL Database.
- Core Languages: Strong proficiency in Python, Spark, and SQL.
- Technical & Architectural Familiarity
- Governance Tools: Practical experience using Azure Purview for governance and cataloguing.
- Languages & Infrastructure: Working knowledge or exposure to R, YAML, and Java Script within data workflows.
- Streaming & Dev
Ops: Experience with event-driven data (e. g., Kafka, Azure Event Hubs) and modern Dev Ops tooling (Azure Dev Ops, Git, Docker, Kubernetes).
- Data Layering: Solid understanding of both SQL and No SQL database design, data warehousing principles, and data modelling techniques.
- Domain & Interpersonal Skills
- Industry Background: Experience working within financial services, central banking, or an economic data environment is highly advantageous.
- Communication: Exceptional ability to bridge the gap between technical infrastructure and economic/analytical business logic.
- Certifications (Preferred): Microsoft Certified: Azure Data Engineer Associate or Databricks certifications
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Velocity Talent!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Engineer at Velocity Talent.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Velocity Talent.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Engineer at Velocity Talent, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Data Engineer in London
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 Velocity Talent, 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 Velocity Talent. 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 Velocity Talent
✨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 Velocity Talent!
✨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.
