SNOWFLAKE DATA ENGINEER - HEALTHCARE - AZURE, POWER BI, SQL SSRS, PYTHON

Talent Leaders
Birmingham
1 month ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer - Snowflake Cortex (Contract)

Data Engineer

Urgent | Contract Senior Data Engineer - Azure + Databricks + Snowflake

Data Engineer

Senior Data Engineer

Lead Data Engineer (AWS & Snowflake)

SNOWFLAKE DATA ENGINEER - HEALTHCARE - AZURE, POWER BI, SQL, SSRS, PYTHON

Progressive, forward thinking, innovative international private healthcare organisation urgently require a talented Data Engineer to help then shape the future state of the organisation

You will:

  • Help create and develop data as they move forward into their new Snowflake environment to ensure they deliver critical data in an accurate and timely information to the rest of the organisation.
  • Develop and implement data pipelines that extract, transform and load data using into our Snowflake environment for use with reporting tools such as Power BI and SSRS.

You are:

  • An Experienced Snowflake Data Engineer
  • With in depth experience of: Azure, Snowflake, Power BI, SSRS
  • SQL, ETL
  • Python
  • Aptitude to Solve Complex Problems

In return you will get the opportunity to have huge impact in developing, shaping and leading the future state & ways of working of an innovative forward-thinking values driven organisation making a difference.

Shortlisting today

Salary: £55k-65k + Excellent Benefits

Location: Birmingham - Flexible/Hybrid c2-3 days a week in office

Tracking.aspx?DwG3xEplepKy581qf2%2bpiga...

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.

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.