Snowflake Data Engineer | Senior Consultant

Slalom
Manchester
2 weeks ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Staff Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - (Python & SQL)

Senior Data Engineer

About us - Slalom

Slalom is a purpose-led, global business and technology consulting company. From strategy to implementation, our approach is fiercely human. In six countries and 45 markets, we deeply understand our customers—and their customers—to deliver practical, end-to-end solutions that drive meaningful impact. Backed by close partnerships with over 400 leading technology providers, our 13,000+ strong team helps people and organisations dream bigger, move faster, and build better tomorrows for all. We’re honoured to be consistently recognised as a great place to work by Glassdoor and being one of Fortune’s 100 Best Companies to Work For seven years running.


Since opening our doors in London in 2014, and then the launch of our Manchester office in 2019, it’s been an unforgettable journey. What’s more, we’re at an exciting stage of our growth in the UK, and now Ireland, with our first office launching in Dublin this year and we’re looking for great people who want to be part of that adventure. Our employees are at the heart of delivering impactful and meaningful work for our clients and helping them to reach for and realise their vision.


Snowflake Data Engineer

Slalom is seeking an experienced Snowflake Data Engineer to contribute to its growing Data & Analytics practice. The ideal candidate is a professional with experience in designing, developing, validating and communicating enterprise data solutions using Snowflake / AWS / Azure. Deep experience in developing enterprise data management strategies including data lake / warehouse implementations, data movement, data services, data acquisition, data conversion, and archive / recovery.


What will you do?

  • Design and implement “best-in-class” modern data solutions for our clients
  • Analyse, recommend and select technical approaches for solving challenging development and integration problems for clients.
  • Explore emerging technology vendors / features and recommend / advise clients on solution options.

Requirements

  • At least 6-8 years experience as a data engineer and 3+ years hands‑on experience in Snowflake.
  • In‑depth understanding of Snowflake data platform capabilities and features.
  • Strong experience in building enterprise‑graded data pipelines on Snowflake. Experience in various data ingestion and transformation patterns including realtime processing, CDC, and API ingestion.
  • Experience in delivering greenfield projects including infrastructure / environment set‑up on Snowflake.
  • Strong data engineering experience delivering end‑to‑end projects on cloud AWS / Azure.
  • Strong SQL and Python skills.
  • Experience in modern data stack tools like DBT, Airflow / Dagster, etc.
  • Experience in CI / CD tooling.
  • Experience in data architecture concepts such as dimensional modelling, data vault, data mesh.
  • Good understanding of data management and governance concepts such as data quality, metadata management, etc.
  • Knowledge of data science and visualisation.
  • Must have Snowflake SnowProCore Certification.


#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.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 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.