Snowflake Data Engineer

Graduate Career Solutions
Brighton
1 month ago
Applications closed

Related Jobs

View all jobs

Snowflake Data Engineer

Snowflake Data Engineer

Snowflake Data Engineer - Outside IR35 - Hybrid 3 days a week

Data Engineer

Lead Data Engineer

Lead Data Engineer

Graduate Career Solutions provided pay range

This range is provided by Graduate Career Solutions. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

Direct message the job poster from Graduate Career Solutions


Snowflake Data Engineer

Location: Brighton, East Sussex (Hybrid / Remote possible)


Job Type: Permanent, Full-time


About the Role


We are looking for a talented Snowflake Data Engineer to join our growing data team based in Brighton. You’ll play a key role in enhancing our data platform, automating data workflows and enabling data-driven decision-making across the business.


This role is ideal for someone who enjoys working with modern cloud data platforms, has excellent SQL skills, and is keen to build scalable data pipelines and data models.


Key Responsibilities



  • Design, build, and maintain scalable ETL/ELT data pipelines using Snowflake.
  • Administer, optimise and support the Snowflake data platform for performance and cost efficiency.
  • Ingest, transform, and integrate data from multiple sources (e.g., GA4, internal systems). Develop and maintain data models to support analytics, reporting and business use cases.
  • Ensure high data quality, monitoring, testing and documentation of pipelines and models.
  • Collaborate with BI, analytics and engineering teams to ensure data meets business needs.
  • Support data governance, security, compliance and best practices in data engineering.

Required Skills & Experience



  • Hands‑on experience in Snowflake data warehouse development and optimisation.
  • Strong SQL skills for querying, transformation and performance tuning.
  • Experience building and managing ETL/ELT pipelines.
  • Proficiency with at least one scripting/programming language (e.g., Python).
  • Familiarity with modern data engineering tools like dbt, Airflow, Prefect, or similar.
  • Knowledge of cloud platforms (AWS / Azure / GCP).
  • Understanding of data modelling, quality controls and best practices.

Qualifications



  • Degree in Computer Science, Data Engineering, IT or a related field (or equivalent experience).
  • Snowflake certifications or relevant cloud/data engineering certifications are advantageous.

Referrals increase your chances of interviewing at Graduate Career Solutions by 2x


#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 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.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.