Data Engineer

Agilis Recruitment
Nottingham
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Agilis are currently working exclusively with a key client who are a leading technology consultancy in their search for a Data Engineer. This is a fantastic opportunity to join a fast growing, forward thinking company and helping them take their Data engineering to the next level!


Job Description:


We are seeking a highly skilled and motivated Data Engineer to join a dynamic team. The ideal candidate will have a strong background in SQL, Python, ETL processes, and data integration, ideally in Databricks. You will play a crucial role in continuing an exciting project designing, developing, and maintaining data infrastructure to ensure the seamless inflow, data sanitation/consolidation and automated report production for clients.


Key Responsibilities:


Design and Development:


  • Design, develop, and maintain scalable ETL pipelines to process and integrate data from various sources.
  • Implement data validation routines to ensure data quality and integrity.
  • Develop and optimize SQL queries for data extraction, transformation, and loading.


Strategic Solution Design:


Data Integration:


  • Integrate data from multiple sources, including APIs & relational databases.
  • Collaborate with cross-functional teams to gather and understand data requirements.


Database Management:


  • Design and maintain relational database schemas to support business needs.
  • Ensure efficient storage, retrieval, and management of large datasets.


API Management:


  • Develop and maintain APIs for data access and integration.
  • Utilize tools like Postman for API testing and documentation.
  • A good understanding of working with APIs:Ensure robust and efficient API integration and management.


Data bricks Management:


  • Manage permissions and access controls within Databricks to ensure data security and compliance


Data Analytics and Reporting:


  • Work with data analysts to provide clean and well-structured data for analysis.
  • Develop and maintain documentation for data processes and workflows.
  • Develop and maintain automatic report production to ensure seamless delivery of critical data


Collaboration and Communication:


  • Collaborate with colleaguesto gather requirements and translate them into technical specifications.
  • Communicate effectively with team members to ensure alignment on data initiatives


Qualifications:


  • Bachelor's degree or equivalent experience in Computer Science, Information Technology, or a related field.
  • Proven experience as a Data Engineer or in a similar role.
  • Strong proficiency in SQL or Python or ideally both.
  • Experience with ETL processes and tools.
  • Knowledge of data validation routines and data integration techniques.
  • Familiarity with relational database design and management.
  • Experience with API development and testing using tools like Postman.
  • Experience of Databricks or similar data platforms desirable
  • Excellent problem-solving skills and attention to detail.
  • Strong communication and collaboration skills.


for more information please apply using the link or get in touch with Edd @ Agilis Recruitment

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.

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.