Senior Azure Data Engineer

Axis
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - Azure & Snowflake

Senior Data Engineer - Azure

Senior Data Engineer Azure Architecture and Platforms

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Overview

This is your opportunity to join AXIS Capital – a trusted global provider of specialty lines insurance and reinsurance. We stand apart for our outstanding client service, intelligent risk taking and superior risk adjusted returns for our shareholders. We also proudly maintain an entrepreneurial, disciplined and ethical corporate culture. As a member of AXIS, you join a team that is among the best in the industry.

At AXIS, we believe that we are only as strong as our people. We strive to create an inclusive and welcoming culture where employees of all backgrounds and from all walks of life feel comfortable and empowered to be themselves. This means that we bring our whole selves to work.

All qualified applicants will receive consideration for employment without regard to race, color, religion or creed, sex, pregnancy, sexual orientation, gender identity or expression, national origin or ancestry, citizenship, physical or mental disability, age, marital status, civil union status, family or parental status, or any other characteristic protected by law. Accommodation is available upon request for candidates taking part in the selection process.

Responsibilities

The Data & Analytics department transforms raw data into actionable insights to drive informed decision-making and optimize business operations. The Senior Azure Data Engineer will design, implement, and manage scalable data solutions on the Azure platform, ensuring efficient data processing, storage, and retrieval. You will play a key role in modernizing our data architecture, ensuring efficient data integration, and enabling advanced analytics to support critical business decisions. This role will enhance the department's ability to deliver high-quality analytics and maintain robust data infrastructure.

What will you do in this role?

As a Senior Azure Data Engineer, you will be responsible for designing, implementing, and maintaining data storage and processing solutions on the Azure platform. You will work with modern data warehouse (MDW) technologies, big data, and Lakehouse architectures to ensure our data solutions are secure, efficient, and optimized.

Key Responsibilities:

  • Design and implement data solutions using Azure services, including Azure Databricks, ADF, and Data Lake Storage.
  • Develop and maintain ETL/ELT pipelines to process structured and unstructured data from multiple sources. Automate loads using Databricks workflows and Jobs
  • Develop, test and build CI/CD pipelines using Azure DevOps to automate deployment and monitoring of data solutions to all environments. Provide knowledge sharing to data operations teams on release management and maintenance.
  • Manage platform administration, ensuring optimal performance, availability, and scalability of Azure data services.
  • Implement end-to-end data pipelines, ensuring data quality, data integrity and data security.
  • Troubleshoot and resolve data pipeline issues while ensuring data integrity and quality.
  • Implement and enforce data security best practices, including role-based access control (RBAC), encryption, and compliance with industry standards.
  • Collaborate with data scientists, analysts, and business stakeholders to deliver high-quality data solutions.
  • Monitor and optimize Databricks performance, including cost management guidance and cluster tuning.
  • Stay up to date with Azure cloud innovations and recommend improvements to existing architectures.
  • Assist data analysts with technical input.

You may also be required to take on additional duties, responsibilities and activities appropriate to the nature of this role.

About YouRequired Skills & Experience
  • 5 plus years Azure & Data Engineering Expertise
  • Proven experience in designing and managing large-scale data solutions on Microsoft Azure.
  • Unity Catalog Mastery: In-depth knowledge of setting up, configuring, and utilizing Unity Catalog for robust data governance, access control, and metadata management in a Databricks environment.
  • Databricks Proficiency: Demonstrated ability to optimize and tune Databricks notebooks and workflows to maximize performance and efficiency. Experience with performance troubleshooting and best practices for scalable data processing is essential.
  • Strong problem-solving skills, ability to work in agile environments, and effective collaboration with cross-functional teams.
  • Experience with implementing a Data Lakehouse solution with Azure Databricks, data modeling, warehousing, and real-time streaming.
  • Knowledge of developing and processing full and incremental loads.
  • Experience of automated loads using Databricks workflows and Jobs
  • Expertise in Azure Databricks, including Delta Lake, Spark optimizations, and MLflow.
  • Strong experience with Azure Data Factory (ADF) for data integration and orchestration.
  • Hands-on experience with Azure DevOps, including pipelines, repos, and infrastructure as code (IaC).
  • Solid understanding of platform administration, including monitoring, logging, and cost management.
  • Knowledge of data security, compliance, and governance in Azure, including Azure Active Directory (AAD), RBAC, and encryption.
  • Experience working with big data technologies (Spark, Python, Scala, SQL).
  • Strong problem-solving and troubleshooting skills.
  • Excellent communication skills with the ability to collaborate with cross-functional teams to understand requirements, data solutions, data models and mapping documents.
Preferred Qualifications
  • Azure certifications (e.g., Azure Data Engineer Associate, Azure Solutions Architect).
  • Experience with Terraform, ARM templates, or Bicep for infrastructure automation.
  • Experience integrating Azure Data Services with Power BI and AI/ML workflows.
Role Factors

The position is full-time with remote work options, requiring in-office presence three days per week

What we offer

You will be eligible for a comprehensive and competitive benefits package which includes medical plans for you and your family, health and wellness programs, retirement plans, tuition reimbursement, paid annual leave, and much more.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.