Big Data Solutions Architect - Emerging Enterprise & Digital Natives

Databricks
Greater London
11 months ago
Applications closed

Related Jobs

View all jobs

MLOps Field Engineer

Data Engineer, Data Engineer Data Analyst ETL Developer BI Developer Big Data Engineer Analytics Engineer Data Platform Engineer Cloud Data Engineer Azure Data Engineer Data Integration Specialist DataOps Engineer Data Pipeline Engineer

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Lead Data Scientist

Data Science PhD Internship

Data Lead - Artificial Intelligence & Automation (12 Month Fixed-Term Contract)

As a Big Data Solutions Architect (Resident Solutions Architect) on ourProfessional Services team for the Emerging Enterprise & Digital Natives business in EMEA, you will engage with customers on short- to medium-term projects, helping them navigate their big data challenges using the Databricks Platform. You will deliver data engineering, data science, and cloud technology solutions-integrating with customer systems, delivering training, and other technical activities that maximise the value customers derive from their data. As a billable consultant, you will ensure projects are completed on time, within scope, and with a best-in-class customer experience. In this role, you will report to the regional Professional Services Lead.

The impact you will have:

  • Drive high-impact customer projects: Design and build reference architectures, implement production use cases, and create “how-to” guides tailored to the unique needs of fast-moving Emerging Enterprise & Digital Native customers in EMEA.
  • Collaborate on project scoping: Work closely with Engagement Managers and customers to define project scope, schedules, and deliverables for professional services engagements.
  • Enable transformational initiatives: Guide strategic customers through their end-to-end big data journeys—migrating from legacy platforms and deploying industry-leading data and AI applications on the Databricks platform.
  • Consult on architecture & design: Provide thought leadership on solution design and implementation strategies, ensuring customers can successfully evaluate and adopt Databricks.
  • Offer advanced support: Serve as an escalation point for operational issues, collaborating with Databricks Support and Engineering to resolve challenges quickly.
  • Align technical delivery: Partner with cross-functional Databricks teams (Technical, PM, Architecture, and Customer Success) to align on milestones, ensuring customer needs and deadlines are met.
  • Amplify product feedback: Provide implementation insights to Databricks Product and Support teams, guiding rapid improvements in features and troubleshooting for customers.

What we look for:

  • Extensive experience and proficiency in data engineering, data platforms, and analytics with a strong track record of successful projects and in-depth knowledge of industry best practices
  • Comfortable writing code in either Python or Scala
  • Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one
  • Deep experience with distributed computing with Apache Spark and knowledge of Spark runtime internals
  • Familiarity with CI/CD for production deployments
  • Working knowledge of MLOps
  • Design and deployment of performant end-to-end data architectures
  • Experience with technical project delivery - managing scope and timelines.
  • Documentation and white-boarding skills.
  • Experience working with clients and managing conflicts.
  • Build skills in technical areas which support the deployment and integration of Databricks-based solutions to complete customer projects.
  • Travel to customers 30% of the time
  • Databricks Certification

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visithttps://www.mybenefitsnow.com/databricks.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

#J-18808-Ljbffr

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.