Fabric Data Engineer

At WTW, we are a leading global advisory, broking, and solutions company. We work with clients across a wide range of industries, helping them manage risk, optimise benefits, and improve performance. As a Fabric Data Engineer, you will play a key role in leveraging Microsoft Fabric, Azure, and Python to design and build advanced data solutions in the insurance domain.

Location:London, UK
Role:Hybrid Workstyle (Full-time)

Role Overview:

As a Fabric Data Engineer at WTW, you will take ownership of developing and optimising data pipelines, workflows, and ETL processes. You will work with cutting-edge technologies to ensure that data is efficiently processed, stored, and made accessible for analysis. This role is a key part of our data engineering team and requires specific expertise in Microsoft Fabric, Azure, and Python.

Key Responsibilities:

Fabric or Azure Data Engineer (Non-Negotiable):

Lead the design and development of scalable data pipelines and ETL processes using Microsoft Fabric or Azure technologies. Manage and optimise notebooks, pipelines, and workflows to enhance the performance and efficiency of our data architecture.
 

Data Pipeline Development & ETL:

Build and maintain high-quality ETL pipelines to clean, transform, and enrich data from various sources.
Ensure that pipelines are automated, scalable, and fault-tolerant to accommodate large volumes of data.
Experience with Notebooks, Pipelines, and Workflows: Utilise Notebooks (., Jupyter, Databricks) for data exploration, analysis, and reporting.
Design and optimise data workflows to streamline key processing tasks, enhancing operational efficiency.
API Integration & Data Ingestion: Integrate external and internal APIs to ingest data into our systems, ensuring smooth and consistent data integration.
Automate the API data ingestion processes to enhance data consistency and quality.
 

AI Experience (Project-based):

Contribute to projects involving AI, including integrating generative AI or machine learning models within our data workflows. Apply AI technologies to improve data processing and provide deeper insights.

SDLC Awareness:

Adhere to Software Development Life Cycle (SDLC) best practices, including version control, testing, and continuous integration. Collaborate with the team to ensure code quality, review processes, and deployment practices. 

Collaboration & Communication:

Work closely with cross-functional teams and business stakeholders to understand and meet data requirements. Effectively communicate complex technical solutions to both technical and non-technical teams, ensuring alignment with business goals.