Data Engineer

Maclean Moore
Windsor
2 weeks ago
Create job alert

We are partnered with a leading global consultancy that is searching for contractors with the following skillsets to work on a LONG-TERM contract within the ENERGY sector:

ROLE 1:

Location:Windsor

Style:Hybrid

Rate:up to £500 per day (inside IR35)

Duration:6 months (initially – view to extend)

Key responsibilities:

  • Design, implement, and manage Kafka-based data pipelines and messaging solutions to support critical business operations and enable real-time data processing.
  • Configure, deploy, and maintain Kafka clusters, ensuring high availability and scalability to maximize uptime and support business growth.
  • Monitor Kafka performance and troubleshoot issues to minimize downtime and ensure uninterrupted data flow, enhancing decision-making and operational efficiency.
  • Collaborate with development teams to integrate Kafka into applications and services.
  • Develop and maintain Kafka connectors such as JDBC, MongoDB, and S3 connectors, along with topics and schemas, to streamline data ingestion from databases, NoSQL data stores, and cloud storage, enabling faster data insights.
  • Implement security measures to protect Kafka clusters and data streams, safeguarding sensitive information and maintaining regulatory compliance.

Key Skills:

  • Design, build, and maintain reliable, scalable data pipelines. Data Integration, Data Security and Compliance.
  • Monitor and manage the performance of data systems and troubleshoot issues.
  • Strong knowledge of data engineering tools and technologies (e.g. SQL, ETL, data warehousing). Experience in tools like Azure ADF, Apache Kafka, Apache Spark SQL, Proficiency in programming languages such as Python, PySpark.
  • Good written and verbal communication skills.
  • Experience in managing business stakeholders for requirement clarification.

ROLE 2:

Role:Hadoop Big Data Developer

Location:Windsor

Style:Hybrid

Rate:up to £400 per day (inside IR35)

Duration:6 months (initially – view to extend)

Key responsibilities:

  • Work closely with the development team to assess existing Big Data infrastructure.
  • Design and code Hadoop applications to analyze data compilations.
  • Extract and isolate data clusters.
  • Test scripts to analyze results and troubleshoot bugs.
  • Create data tracking programs and documentation.
  • Maintain security and data privacy.

Key Skills:

  • Build, schedule and maintain data pipelines. Good expertise in Pyspark, Spark SQL, Hive, Python, Kafka.
  • Strong experience in Data Collection and Integration, Scheduling, Data Storage and Management, ETL (Extract, Transform, Load) Processes.
  • Knowledge of relational and non-relational databases (e.g., MySQL, PostgreSQL, MongoDB).
  • Good written and verbal communication skills.
  • Experience in managing business stakeholders for requirement clarification.

If you are interested and have the relevant experience, please apply promptly and we will contact you to discuss it further.

Senior Delivery Consultant

London | Bristol | Amsterdam

Seniority level

Mid-Senior level

Employment type

Contract

Job function

Information Technology

Industries

IT Services and IT Consulting

#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer - UK Perm - London Hrbrid

Oracle Java8 Scala Spring Data Engineer London £575d insideIR35

Data Engineer, DE55

Data Engineer - Databricks

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.