Data Engineering Lead - AWS & Snowflake

Datatech
Longford
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineering & DataOps Leader – Azure

Data Engineer — DataOps, Cloud Data Pipelines

AI Engineering Lead - GenAI, MLOps & Production (Hybrid)

Data Engineer, Data Engineer Data Analyst ETL Developer BI Developer Big Data Engineer Analytics Engineer Data Platform Engineer Cloud Data Engineer Azure Data Engineer Data Integration Specialist DataOps Engineer Data Pipeline Engineer

ML & AI Engineering Lead: Generative AI & MLOps

Senior Data Scientist (AWS) — ML & Optimization

Data Engineering Lead - AWS & Snowflake, Hybrid working: 3 days inTW6, Middlesex offices & 2 days homer/remote, Salary: Negotiable to £70.,000 DOE plus 40 % bonus potential, Job Reference: J12869Full UK working rights required/no sponsorship available Looking for a challenge in one of the worlds largest airfreight logistics organisation and a FTSE 100 company?Within the Digital and Information function, the Data Engineering Lead will play a pivotal role in delivering and operating data products. Reporting to the Head of Data, Insights & Operational Research, this position holds significant responsibility within the data leadership team, ensuring our data solutions and business processes are fully aligned and contribute to the vision and strategic direction of the organisation.The successful candidate will join the team at an exciting time. They are in the early stages of a major programme of work to modernise their data infrastructure, tooling and processes to migrate from an on-premise to a cloud native environment and the Data Engineering Lead will be essential to the success of the transformation.Using your strong communication skills combined with a determined attitude you will be responsible for managing and developing a team of data engineers to develop effective and innovative solutions aligning to our architectural principles and the business need. You will ensure the team adheres to best practices in data engineering and contributes to the continuous improvement of our data systems.Key responsibilities for this role include:• Lead the design, development, and deployment of scalable and efficient data pipelines and architectures.• Manage and mentor a team of data engineers, ensuring a culture of collaboration and excellence.• Manage demand for data engineering resources, prioritising tasks and projects based on business needs and strategic goals.• Monitor and report on the progress of data engineering projects, addressing any issues or risks that may arise.• Collaborate closely with Analytics Leads, Data Architects, and the wider Digital and Information team to ensure seamless integration and operation of data solutions.• Develop and implement a robust data operations capability to ensure the smooth running and reliability of our data estate.• Drive the adoption of cloud technologies and modern data engineering practices within the team.• Ensure data governance and compliance with relevant regulations and standards.• Work with the team to define and implement best practices for data engineering, including coding standards, documentation, version control.Skills• Expert in SQL and database concepts including performance tuning and optimisation• Solid understanding of data warehousing principles and data modelling practice• Strong engineering skills, preferably in the following toolsets- AWS services (S3, EC2, Lambda, Glue)- ETL Tools (e.g. Apache Airflow)- Streaming processing tools (e.g. Kinesis)- Snowflake- Python• Excellent knowledge of creation and maintenance of data pipelines• Strong problem-solving and analytical skills, with the ability to troubleshoot and resolve complex data-related issues• Proficient in data integration techniques including APIs and real-time ingestion• Excellent communication and collaboration skills to work effectively with cross-functional teams• Capable of building, leading, and developing a team of data engineers• Strong project management skills and an ability to manage multiple projects and prioritiesExperience• Experienced and confident leadership of data engineering activities (essential)• Expert in data engineering practise on cloud data platforms (essential)• Background in data analysis and preparation, including experience with large data sets and unstructured data (desirable)• Knowledge of AI/Data Science principles (desirable)If you would like to hear more, please do get in touch.Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes! If you have a friend or colleague who would be interested in this role, please refer them to us. For each relevant candidate that you introduce to us (there is no limit) and we place, you will be entitled to our general gift/voucher scheme.Datatech is one of the UK's leading recruitment agencies in the field of analytics and host of the critically acclaimed event, Women in Data. For more information, visit our website: (url removed)

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.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.