National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Senior Data Engineer

Mastek
Greater London
2 weeks ago
Create job alert

Job Title: Senior Data Engineer

Location: London, UK (3 days in the office)
SC Cleared: Required
Job Type: Full-Time
Experience: 8+ years

Job Summary :

We are seeking a highly skilled and experienced Senior Data Engineer to join our team and contribute to the development and maintenance of our cutting-edge Azure Databricks platform for economic data. This platform is critical for our Monetary Analysis, Forecasting, and Modelling activities. The Senior Data Engineer will be responsible for building and optimising data pipelines, implementing data transformations, and ensuring data quality and reliability. This role requires a strong understanding of data engineering principles, big data technologies, cloud computing (specifically Azure), and experience working with large datasets.

Key Responsibilities :

Data Pipeline Development & Optimisation:
Design, develop, and maintain robust and scalable data pipelines for ingesting, transforming, and loading data from various sources (e.g., APIs, databases, financial data providers) into the Azure Databricks platform.
Optimise data pipelines for performance, efficiency, and cost-effectiveness.
Implement data quality checks and validation rules within data pipelines.

Data Transformation & Processing:
Implement complex data transformations using Spark (PySpark or Scala) and other relevant technologies.
Develop and maintain data processing logic for cleaning, enriching, and aggregating data.
Ensure data consistency and accuracy throughout the data lifecycle.

Azure Databricks Implementation:
Work extensively with Azure Databricks Unity Catalog, including Delta Lake, Spark SQL, and other relevant services.
Implement best practices for Databricks development and deployment.
Optimise Databricks workloads for performance and cost.
Need to program using the languages such as SQL, Python, R, YAML and JavaScript

Data Integration:
Integrate data from various sources, including relational databases, APIs, and streaming data sources.
Implement data integration patterns and best practices.
Work with API developers to ensure seamless data exchange.

Data Quality & Governance:
Hands on experience to use Azure Purview for data quality and data governance
Implement data quality monitoring and alerting processes.
Work with data governance teams to ensure compliance with data governance policies and standards.
Implement data lineage tracking and metadata management processes.

Collaboration & Communication:
Collaborate closely with data scientists, economists, and other technical teams to understand data requirements and translate them into technical solutions.
Communicate technical concepts effectively to both technical and non-technical audiences.
Participate in code reviews and knowledge sharing sessions.

Automation & DevOps:
Implement automation for data pipeline deployments and other data engineering tasks.
Work with DevOps teams to implement and Build CI/CD pipelines, for environmental deployments.
Promote and implement DevOps best practices.

Essential Skills & Experience:
10+ years of experience in data engineering, with at least 3+ years of hands-on experience with Azure Databricks.
Strong proficiency in Python and Spark (PySpark) or Scala.
Deep understanding of data warehousing principles, data modelling techniques, and data integration patterns.
Extensive experience with Azure data services, including Azure Data Factory, Azure Blob Storage, and Azure SQL Database.
Experience working with large datasets and complex data pipelines.
Experience with data architecture design and data pipeline optimization.
Proven expertise with Databricks, including hands-on implementation experience and certifications.
Experience with SQL and NoSQL databases.
Experience with data quality and data governance processes.
Experience with version control systems (e.g., Git).
Experience with Agile development methodologies.
Excellent communication, interpersonal, and problem-solving skills.
Experience with streaming data technologies (e.g., Kafka, Azure Event Hubs).
Experience with data visualisation tools (e.g., Tableau, Power BI).
Experience with DevOps tools and practices (e.g., Azure DevOps, Jenkins, Docker, Kubernetes).
Experience working in a financial services or economic data environment.
Azure certifications related to data engineering (e.g., Azure Data Engineer Associate).

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Azure - Leeds

Senior Data Engineer - Snowflake - £100,000 - London - Hybrid

National AI Awards 2025

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 to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.