Solutions Engineer (Data Engineering and/or Data Warehousing)

Databricks
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Science Lead / Manager

Machine Learning Engineer

MLOps / ML Engineer

Senior Computer Vision Engineer

Senior Machine Learning Engineer – Computer Vision

Req ID FEQ425R127

At Databricks, our core values are at the heart of everything we do; creating a culture of proactiveness and a customer-centric mindset guides us to create a unified platform that makes data science and analytics accessible to everyone. We aim to inspire our customers to make informed decisions that push their business forward. We provide a user-friendly and intuitive platform that makes it easy to turn insights into action and fosters a culture of creativity, experimentation, and continuous improvement. You will be an essential part of this mission, using your technical expertise to demonstrate how our Databricks Data Intelligence Platform can help customers solve their complex data challenges. You'll work with a collaborative, customer-focused team that values innovation and creativity, using your skills to create customized solutions to help our customers achieve their goals and guide their businesses forward. Join us in our quest to change how people work with data and make a better world!

You will be reporting to the Manager, Field Engineering.

The impact you will have:

Form successful relationships with clients throughout your assigned territory to provide technical and business value in collaboration with an Account Executive and a Senior Solutions Architect. Gain excitement from clients about Databricks through hands-on evaluation and Spark programming, integrating with the wider cloud ecosystem and 3rd party applications. Contribute to building the Databricks technical through engagement at workshops, seminars, and meet-ups. Become a Big Data Analytics advisor on aspects of architecture and design. Support your customers by authoring reference architectures, how-tos, and demo applications. Develop both technically and in the pre-sales aspect with the goal of becoming an independently operating Solutions Architect.

What we look for:

Experience, technical customer-facing and with a background in Data Engineering (Spark, Databricks, etc) and / or Data Warehousing (BI, DWH, SQL, PowerBI) skills. You will be working in the following any one of the following vertical sectors: Communications, Media and EntertainmentManufacturing, Energy and IndustrialsTravel, Transport and LogisticsFinancial Services and InsuranceHealthcare and Life SciencesPublic SectorConsumer Package Goods (CPG) / Retail Any experience of Pre-sales or post-sales experience working with external clients Familiarity working with clients, creating a narrative, answering customer questions, aligning the agenda with important interests, and achieving tangible outcomes. Ability to independently deliver a technical proposition, identify customers' pain points, and explain important areas for business value to develop a trusted advisor skillset. Code in a core programming language such as Python, Java, or Scala. Knowledgeable in a core Big Data Analytics domain with some exposure to advanced proofs-of-concept and an understanding of a major public cloud platform (AWS, GCP, Azure). Experience diving deeper into solution architecture and design. The role requires 30% travel to customer sites in the UK and to the London offices. Nice to have: Databricks Certification

Benefits

Private medical insurance Private dental insurance Health Cash Plan Life, income protection & critical illness insurance Pension PlanEquity awards Enhanced Parental Leaves Fitness reimbursement Annual career development fund Home office & work headphones reimbursement Business travel accident insurance Mental wellness resources Employee referral bonus

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.