Senior Data Engineer

Mastek
London
1 month 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

Senior Data Engineer - Data Migration

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Navigating Data Science Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Data science has taken centre stage in the modern workplace. Organisations rely on data-driven insights to shape everything from product innovation and customer experience to operational efficiency and strategic planning. As a result, there is a growing need for skilled data scientists who can analyse large volumes of data, build predictive models, communicate findings effectively, and collaborate cross-functionally. If you are looking to accelerate your data science career—or even land your first role—attending data science career fairs can be a game-changer. Unlike traditional online applications, face-to-face interactions let you showcase your personality, passion, and communication skills in addition to your technical expertise. However, to stand out in a busy environment, you need a clear strategy: from polishing your personal pitch and asking thoughtful questions to following up with a memorable message. In this article, we’ll guide you through every step of making a strong impression at data science career fairs in the UK and beyond.

Common Pitfalls Data Science Job Seekers Face and How to Avoid Them

Data science has become a linchpin for decision-making and innovation across countless industries, from finance and healthcare to tech and retail. The demand for data scientists in the UK continues to climb, with businesses seeking professionals who can interpret complex datasets, build predictive models, and communicate actionable insights. Despite this high demand, the job market can be extremely competitive—and many applicants unknowingly fall into avoidable traps. Whether you’re an aspiring data scientist fresh out of university, a professional transitioning from a quantitative role, or a seasoned analyst looking to expand your skill set, it’s crucial to navigate your job search effectively. In this article, we explore the most common pitfalls data science job seekers face and provide pragmatic advice to help you stand out. By refining your CV, portfolio, interview strategies, and communication skills, you can significantly increase your chances of landing a rewarding data science role. If you’re looking for your next data science job in the UK, don’t forget to explore the listings at Data Science Jobs. Read on to discover how to avoid critical mistakes and position yourself for success.

Career Paths in Data Science: From Entry-Level Analysis to Leadership and Beyond

Data is the lifeblood of modern business, and Data Scientists are the experts who turn raw information into strategic insights. From building recommendation engines to predicting market trends, the impact of data science extends across virtually every industry—finance, healthcare, retail, manufacturing, and beyond. In the UK, data-driven decision-making is critical to remaining competitive in a global market, making data science one of the most sought-after career paths. But how does one launch a career in data science, and how can professionals progress from entry-level analysts to senior leadership roles? In this comprehensive guide, we’ll explore the typical career trajectory, from junior data scientist to chief data officer, discussing the key skills, qualifications, and strategic moves you need to succeed. Whether you’re a recent graduate, transitioning from another technical field, or an experienced data scientist aiming for management, you’ll find actionable insights on forging a successful career in the UK data science sector.