At a Glance
- Tasks: Lead the design and implementation of scalable data solutions in the insurance domain.
- Company: Fractal, a strategic AI partner to Fortune 500 companies, recognised as a Great Place to Work.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborate with cross-functional teams and optimise data solutions using cutting-edge technologies.
- Why this job: Join a visionary team that empowers imagination with intelligence in a dynamic environment.
- Qualifications: Proven experience in data engineering, ETL/ELT processes, and Azure ecosystem expertise.
The predicted salary is between 80000 - 100000 £ per year.
- Lead Azure Data Engineering Architect – Insurance Domain (Lloyds Marketplace)
- London
- Onsite - Hybrid
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise.
Fractal is building a world where individual choices, freedom, and diversity are the greatest assets.
An ecosystem where human imagination is at the heart of every decision.
Where no possibility is written off, only challenged to get better.
We believe that a true Fractalite is the one who empowers imagination with intelligence.
Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.
Please visit Fractal | Intelligence for Imagination for more information about Fractal
Responsibilities
- Design and implement scalable data migration strategies and ETL/ELT data pipelines.
- Strong Insurance domain experience, preferably within the Lloyd’s marketplace
- Good understanding of tools and platforms used across the Lloyd’s insurance ecosystem
- Architect and optimize data solutions using Py Spark and Datamart (GOS – Oracle as a Service).
- Develop and maintain data platforms leveraging Snowflake and Azure services (Blob Storage, Data Lake, Synapse Analytics, Databricks).
- Integrate serverless components such as Azure Functions for automation and orchestration.
- Ensure system monitoring and performance optimization using Azure Monitor and Grafana.
- Collaborate with cross-functional teams to define data architecture standards and best practices.
Requirements
- Proven experience in data engineering and architecture with strong expertise in ETL/ELT processes.
- Hands-on experience with Py Spark, Snowflake, and Oracle-based data solutions.
- Proficiency in Azure ecosystem: Blob Storage, Data Lake, Synapse Analytics, Databricks, Functions, and Monitor.
- Strong understanding of data modeling, migration strategies, and performance tuning.
- Familiarity with monitoring tools such as Grafana for system health and analytics.
- Excellent problem-solving skills and ability to lead technical teams
Lead Azure Data Engineering Architect in Slough employer: Fractal
Fractal is an exceptional employer that values innovation and collaboration, offering a dynamic work culture where your expertise in CPG data strategy can truly shine. With a flexible hybrid work arrangement in Greater London, employees enjoy a healthy work-life balance while having access to continuous growth opportunities and the chance to engage with senior stakeholders on impactful projects. Join us to be part of a forward-thinking team that prioritises professional development and fosters a supportive environment for all.
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We think you need these skills to ace Lead Azure Data Engineering Architect in Slough
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!
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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.
