Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Snowflake Lead

Hyqoo
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Analyst | Commodities & Energy Trading - Front office | Up to £115k + Bonus, Benefits |......

Lead Architect - Azure / Data Engineering

Lead Data Engineer (Data Science Team)

Principal Data Engineer

Senior Data Engineer | Commodities & Energy Trading | ClickHouse-Centric Data Platform | Next-Gen Lakehouse | Python, SQL | Up to £89K + Bonus + Benefits

Principal Data Scientist (H/F)

Tiitle - Snowflake Data Lead

Type - 6 month + Contract to hire

Location - Remote


Key Responsibilities:


- Lead and oversee the Snowflake data platform, ensuring top-notch scalability, performance, and security.

- Partner with cross-functional teams, including data engineering and IT security, to establish and enforce data governance and security standards.

- Monitor and optimize Snowflake's performance and cost efficiency.

- Conduct system audits, refine procedures, and enhance processes for better platform dependability and operational excellence.

- Oversee data management tasks such as storage, archiving, recovery, and backups.

- Spearhead the design and development of scalable data structures, pipelines, and performant ELT processes.

- Facilitate the integration of Snowflake with various data management and analytical tools.

- Offer technical leadership for deploying and maintaining key Snowflake features, including Snowpipe, virtual warehouses, and data sharing capabilities.

- Create and maintain comprehensive documentation for the data platform's setup, operations, and troubleshooting.

- Educate and mentor team members on Snowflake best practices and new functionalities.


Required Qualifications:


- Bachelor’s degree in Computer Science, Information Technology, or related field.

- A minimum of 6 years of hands-on experience with Snowflake data platform administration and architecture.

- Proficiency in SQL and scripting languages such as Python or JavaScript.

- Demonstrable expertise in implementing and managing virtual warehouses, resource monitors, and data security within Snowflake.

- Solid experience in data modeling, data warehousing, and constructing ELT workflows.

- Excellent problem-solving, analytical mindset, and the ability to troubleshoot complex issues.

- Strong communication and collaboration skills, with a track record of effective teamwork.

- Preferred certifications: Snowflake Data Analyst, Snowflake Data Engineer, or Snowflake Data Scientist.


Desired Skills and Knowledge:


- In-depth knowledge of ELT best practices and data integration techniques.

- Familiarity with data security, compliance standards, and data privacy regulations.

- Experience with cloud infrastructure and services, especially relating to data storage and computing.

- Knowledge of JavaScript and front-end technologies is a plus for developing custom user interfaces or integrations.


Tools and Technology Required:


- Snowflake Data Platform

- SQL and scripting languages (Python, JavaScript)

- Data modeling and ELT tools

- Data governance and security tools

- Performance monitoring and optimization software

- Cloud services and infrastructure

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.