Data Engineer - Hybrid/Bristol - Up to £55,000

Job Title: Data Engineer

Location: Hybrid/Bristol (Min. 2 days per week)

Remuneration: £45,000 - £55,000 per annum

Responsibilities:

• Pipeline Development and Optimisation: Continuously assess and improve the performance of data enrichment pipelines, ensuring their efficiency, dependability, and scalability.
  
  
• Data Management: Design and implement robust processes for data ingestion and cleaning, supporting machine learning and analytical models.
  
  
• Collaborative Problem-Solving: Work closely with data scientists to troubleshoot, identify, and resolve complex issues, ensuring smooth operations across the board.
  
  
• Model Development and Deployment: Assist in building, training, monitoring, and deploying cutting-edge machine learning models.
  
  
• Stay Current with AI/ML Trends: Keep up to date with the latest advancements in data processing, AI, and ML, and incorporate them into our client's processes to improve efficiency.
  
  
• Adaptable Approach: Collaborate across different functions as required, including taking on backend development tasks like API creation with support from more senior engineers.
  
  Our client, a leading player in the risk industry, is seeking a skilled and motivated Data Engineer to join their innovative team in Bristol. With a minimum requirement of two days per week in the office, this position offers the opportunity to contribute to the development and optimisation of AI/ML-powered data enrichment workflows and infrastructure. We are seeking someone with a strong Python expertise, a creative mindset, and a passion for working with modern AI/ML systems.
  
  To succeed in this role, you should have a Bachelor's degree (or equivalent) in computer science, mathematics, or a related field, along with at least three years of relevant experience. You should have a proven ability to design, build, and deploy machine learning models and/or data pipelines, and be proficient in Python with hands-on experience in PySpark or Pandas.
  
  In addition to technical expertise, we value strong analytical skills, the ability to address data quality issues and optimise model performance, and the willingness to think creatively and independently to solve complex problems. Experience in deploying and managing machine learning models in production environments, knowledge of advanced techniques such as gradient boosting and large-scale text embedding models, and familiarity with tools such as Databricks, Git, CI/CD pipelines, and software testing methodologies are also preferred qualifications.
  
  If you are ready to join a dynamic and innovative team, apply now and take the next step in your career as a Data Engineer!
  
  Please note that only successful applicants will be contacted.
  
  Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities to apply. Adecco is committed to building a supportive environment for you to explore the next steps in your career. If you require reasonable adjustments at any stage, please let us know and we will be happy to support you.
  
  KEYWORDS:
  
  Python / Data pipelines / Data enrichment / PySpark / Pandas / Machine learning / AI/ML / Model deployment / Data cleaning / Data ingestion / Databricks / Git / CI/CD pipelines / API development / Gradient boosting / Text embedding models / Production ML / Software engineering / Data processing / Analytical models / Data science / Model monitoring / Testing / Deployment techniques / Python / Data pipelines / Data enrichment / PySpark / Pandas / Machine learning / AI/ML / Model deployment / Data cleaning / Data ingestion / Databricks / Git / CI/CD pipelines / API development / Gradient boosting / Text embedding models / Production ML / Software engineering / Data processing / Analytical models / Data science / Model monitoring / Testing / Deployment techniques / Python / Data pipelines / Data enrichment / PySpark / Pandas / Machine learning / AI/ML / Model deployment / Data cleaning / Data ingestion / Databricks / Git / CI/CD pipelines / API development / Gradient boosting / Text embedding models / Production ML / Software engineering / Data processing / Analytical models / Data science / Model monitoring / Testing / Deployment techniques