Snowflake Data Architect

Test Yantra
Hertfordshire
1 day ago
Create job alert
Required Technical Skills

  • Data Platform

    • Snowflake
    • Deep knowledge of Snowflake architecture, performance tuning, data sharing, security, and workload optimization.


  • Cloud Platform

    • Strong experience with Amazon Web Services, including:

      • S3
      • IAM
      • AWS Glue
      • Lambda
      • CloudWatch
      • Data Transformation


    • Strong experience with DBT for enterprise-scale data modelling, testing, and transformation pipelines.


  • Programming / Query

    • Strong expertise in SQL for data transformation and performance optimization
    • Python (preferred) for automation and data engineering tasks.


  • Data Engineering

    • Enterprise ETL / ELT pipeline architecture
    • Data warehousing and enterprise data modelling
    • Dimensional modelling (Star Schema, Snowflake Schema)
    • Data pipeline scalability and reliability design.


  • AI / Data Science Exposure

    • Experience supporting AI/ML data pipelines and data preparation for machine learning models.
    • Understanding of predictive analytics, recommendation engines, and customer behaviour analytics.
    • Ability to design AI-ready data platforms for future analytics use cases.


  • Preferred Skills

    • Experience with Apache Airflow for pipeline orchestration.
    • Knowledge of CI/CD pipelines, DevOps, and Git-based development workflows.
    • Experience with data governance, metadata management, and enterprise data catalog tools.
    • Experience with BI tools such as:

      • Tableau
      • Microsoft Power BI.


    • Domain experience in hospitality, travel, or hotel systems, including reservation systems, guest analytics, and operational reporting.



#J-18808-Ljbffr

Related Jobs

View all jobs

Snowflake Data Architect

Snowflake Data Architect - (M/F/D)

Snowflake Data Architect for AI-Ready Enterprise Pipelines

Snowflake Data Architect & Cloud ETL Engineer

Snowflake Data Architect - (M/F/D)

Snowflake Data Architect – AWS, DBT & Analytics

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.

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.

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.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.