Senior Data Engineer

Nottingham
1 month ago
Applications closed

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

Join our client in embarking on an ambitious data transformation journey using Databricks, guided by best practice data governance and architectural principles. To support this, we are recruiting for talented data engineers. As a major UK energy provider, our client is committed to 100% renewable energy and sustainability, focusing on delivering exceptional customer experiences.

It is initially a 3-month contract with potential to be extended. The role is Hybrid, with one day a week being based in their Nottingham office, this is negotiable. It is a full-time role, 37 hours per week.

Accountabilities:

  • Develop and maintain scalable, efficient data pipelines within Databricks, continuously evolving them as requirements and technologies change.
  • Build and manage an enterprise data model within Databricks.
  • Integrate new data sources into the platform using batch and streaming processes, adhering to SLAs.
  • Create and maintain documentation for data pipelines and associated systems, following security and monitoring protocols.
  • Ensure data quality and reliability processes are effective, maintaining trust in the data.
  • Be comfortable with taking ownership of complex data engineering projects and develop appropriate solutions in accordance with business requirements.
  • Able to work closely with stakeholders and managing their requirements.
  • Actively coach and mentor others in the team and foster a culture of innovation and peer review within the team to ensure best practice.

    Knowledge and Skills:
  • Extensive experience of Python preferred, including advanced concepts like decorators, protocols, functools, context managers, and comprehensions.
  • Strong understanding of SQL, database design, and data architecture.
  • Experience with Databricks and/or Spark.
  • Knowledgeable in data governance, data cataloguing, data quality principles, and related tools.
  • Skilled in data extraction, joining, and aggregation tasks, especially with big data and real-time data using Spark.
  • Capable of performing data cleansing operations to prepare data for analysis, including transforming data into useful formats.
  • Understand data storage concepts and logical data structures, such as data warehousing.
  • Able to write repeatable, production-quality code for data pipelines, utilizing templating and parameterization where needed.
  • Can make data pipeline design recommendations based on business requirements.
  • Experience with data migration is a plus.
  • Open to new ways of working and new technologies.
  • Self-motivated with the ability to set goals and take initiative.
  • Driven to troubleshoot, deconstruct problems, and build effective solutions.
  • Experience of Git / Version control
  • Experience working with larger, legacy codebases
  • Understanding of unit and integration testing
  • Understanding and experience with CI/CD and general software development best practices
  • A strong attention to detail and a curiosity about the data you will be working with.
  • A strong understanding of Linux based tooling and concepts
  • Knowledge and experience of Amazon Web Services is essential

    Please note:
    Should your application be successful, and you are offered the role, a number of pre-employment checks need to be carried out before your appointment can be confirmed. Any assignment offer with our client will be subject to a satisfactory checking report from the Disclosure Barring Service.
    This vacancy is being advertised by Rullion Ltd acting as an employment business.
    Since 1978, Rullion has been securing exceptional candidates for a range of clients; from large well-known brands, to SMEs and start-ups. As a family-owned business, Rullion's approach is credible and honest, focused on building long-lasting relationships with both clients and candidates.
    We celebrate and support diversity and are committed to ensuring equal opportunities for both employees and applicants.

    Rullion celebrates and supports diversity and is committed to ensuring equal opportunities for both employees and applicants

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.