AWS Databricks Data Architect

City of London
7 months ago
Applications closed

Related Jobs

View all jobs

Healthcare Data Architect (Hybrid) — AWS & Databricks

Big Data Solutions Architect, Spark (Professional Services)

Big Data Solutions Architect (Professional Services)

Big Data Solutions Architect (Professional Services)

Big Data Solutions Architect at Databricks – London, United Kingdom

Big Data Solutions Architect, Professional Services

My client is based in the London area are currently looking to recruit for an experienced AWS Databricks Data Architect to join their team. They are one of the leaders within the consulting space and are currently going through a period of growth and are looking for an experienced Data Architect to join their team. They are backed by a huge Multi National equity firm who are looking to bolster my clients financial position. They are expected to see year on year growth, which will allow them to implement and utilise the most in demand and cutting edge technology on the market right now. They have just implemented Gen AI within their organisation are looking to utilise the newest technologies on the market.

Your role will include:

Responsible for designing and implementing effective Architectural solutions around the AWS Severless Technologies (S3, Lambda, Athena, Kafka) and Databricks including Data Lake and Data Warehousing.
Assess database implementation procedures to ensure they comply with GDPR and data compliance.
Guide, influence and challenge the technology teams and stakeholders to understand the benefits, pros and cons of solution options.
Agree and set the technical direction for the data platform landscape and solutions, for the short and long term, in collaboration with delivery and engineering teams.
This is a hands on role which requires extensive exposure to cloud technologies (AWS and Azure).

The right candidate will have extensive knowledge of writing codes, building data pipelines and doing digital transformation and ingestion a certain tech suite. Extensive experience in implementing solutions around the AWS cloud environment (S3, Databricks, Athena, Glue),
In depth understanding of Workflows, Asset Bundles, SQS, EKS, Terraform,
Excellent understanding of Data Modelling & Kinesis
An understanding of SQL/database management.
Strong hands-on experience in Data Warehouse and Data Lake technologies preferably around AWS.

My client is providing access to;

Hybrid 2 days (London),
28 Days Holiday, Plus Bank Holiday
Private Medical Health
Pension Scheme
And More...

This role is an urgent requirement, there are limited interview slots left, if interested send an up to date CV to Shoaib Khan - (url removed) or call (phone number removed) for a catch up in complete confidence.

Frank Group's Data Teams offer more opportunities across the UK than any other recruiter We're the proud sponsor and supporter of SQLBits, AWS RE:Invent, Power Platform World Tour, the London Power BI User Group, Newcastle Power BI User Group and Newcastle Data Platform and Cloud User Group

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.