Senior Data Engineer

developrec
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineering Lead: Lakehouse & DataOps

Senior Data Engineer (AI & MLOps, AWS, Python)

Senior Data Science Engineer

Senior Data Science Engineer

Senior Data Science Engineer ML & Data Strategy

Senior Data Science Engineer: ML, Data, Impact

Senior Data Engineer - London, 2x Weekly - £65,000-£70,000


Our client is seeking a Senior Data Engineer to join their growing team in London. In this critical role, you will lead the design, development, and optimization of data pipelines and systems, with a strong focus on Snowflake and Databricks platforms. Your work will involve creating robust ETL processes using Python, enabling the organization to leverage its data more effectively. A background in AI/ML is highly advantageous, as the company is looking to integrate advanced analytics into their data operations.


What You'll Do

  • Design, build, and optimise data pipelines using Snowflake and Databricks to support the company’s data infrastructure.
  • Develop ETL processes in Python to automate the ingestion, transformation, and integration of data from various sources.
  • Collaborate with data scientists and analysts to implement AI/ML models into the data architecture, enhancing the company’s analytical capabilities.
  • Work with cross-functional teams to gather data requirements and translate them into scalable, efficient solutions.
  • Ensure data quality, reliability, and performance across all data systems.
  • Continuously assess and improve existing data processes to enhance efficiency and scalability.
  • Stay up to date with emerging technologies and best practices in data engineering, particularly in Snowflake, Databricks, and AI/ML.


About You

  • Extensive experience in data engineering with a strong focus on Snowflake and Databricks.
  • Proficient in Python, with a proven track record of developing and maintaining ETL processes.
  • Solid understanding of data architecture and best practices for building scalable, high-performance data systems.
  • Experience with AI/ML technologies and a passion for integrating advanced analytics into data operations.
  • Strong problem-solving skills and the ability to work independently in a fast-paced environment.
  • Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
  • A collaborative team player with experience working across multiple departments to achieve common goals.


Bonus Points If You Have

  • Hands-on experience with AI/ML frameworks and integrating machine learning models into data pipelines.
  • Familiarity with additional cloud data platforms or tools.
  • Experience in a senior or lead role, mentoring junior engineers and guiding technical projects.

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.