Data Solution Architect

Epam
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Data Scientist / AI Engineer

Senior Practice Lead - Data Science_ UK

Machine Learning Engineer (Manager)

Head of DevOps and DataOps

Description

About the Role



Are you an avid technologist who enjoys solving complex data and technical consulting challenges? Are you hungry to thrive in an entrepreneurial environment as part of a rapidly expanding and very successful global engineering company?
If so, then EPAM is looking for a Data Solutions Architect with a rich background in enterprise stacks, specializing in a variety of platforms with a focus on back-end systems, high-load and throughput environments, Analytics and Digitization. The ideal candidate will possess comprehensive knowledge in Data Strategy, Management, Architecture, Governance, Technology Architecture, Data Product Lifecycle Management and have cross industry experience, ideally in Pharma or Financial Services.

This role is perfect for those eager to participate and contribute to our rapid growth in Western Europe.

Responsibilities

Consult with business colleagues to identify requirements and unmet needs that can be addressed through Data Solutions Create and present solution architecture documents with deep technical details to customer and implementation teams Lead implementation of the solutions from establishing project requirements and goals to a live solution Maintain a strong understanding of technical solutions and architecture design trends and best practices, staying on cutting edge of data technologies Participate in specific pre-sale activities to prepare technical proposals on customer requests Participate in Data and Big Data initiatives at a company level built around partnerships, adoption, training and campaigns among others Constantly grow expertise by gathering and monitoring available EPAM project experience and ongoing projects with clients from different business domains to drive further EPAM business in Data field

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field Extensive experience in either Snowflake and/or Databricks Familiarity with Machine Learning and Large Language Models (LLM) Extensive experience with building Data Warehouses, Data Lakes Extensive experience in Data Quality, Data Modelling and Various types of Analytics Experienced in at least one of the Cloud providers (AWS, Azure) and at least one traditional Data Platform stack Knowledge of high load and high throughput Data Platform architectures and infrastructures Extensive hands-on experience with design/development on Python/Scala/Java and SQL General experience in continuous delivery tools and technologies General experience in Serverless Designs, MPP Architectures, Containers and Resource Management systems Proven in research, comparison and selection of tools/technologies/approaches to be used Strong communication skills, experienced in team coordination skills and solution implementation supervision

Our Benefits Include

A competitive group pension plan and protection benefits including life assurance, income protection and critical illness cover Private medical insurance and dental care Cyclescheme, Techscheme and season ticket loans Employee assistance program Great learning and development opportunities, including in-house professional training, career advisory and coaching, sponsored professional certifications, well-being programs, LinkedIn Learning Solutions and much more EPAM Employee Stock Purchase Plan (ESPP) Various perks such as gym discounts, free Wednesday lunch in-office, on-site massages and regular social events Certain benefits and perks may be subject to eligibility requirements and may be available only after you have passed your probationary period

About EPAM

EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential

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.