Data Engineering Lead (Snowflake & AWS Environment)

Middlesex
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Engineering Lead

Data Engineering Lead / Data Architect

Lead Enterprise Architect, Advanced Analytics

CDD Platform Lead

CDD Platform Lead

CDD Platform Lead

Data Engineering Lead (Snowflake & AWS Environment)

Hybrid working: 3 days in TW6, Middlesex offices & 2 days home/remote
Salary: Negotiable to £70,000 DOE plus 40 % bonus potential
Job Ref: J12869

Please note we can only accept applications from those with current UK working rights for this role, this client cannot offer visa sponsorship.

An exciting opportunity has arisen within a FTSE 100 company for a Data Engineering Lead to play a pivotal role in operating and delivering the organisation's data products. This position holds significant responsibility within the data leadership team, ensuring the data solutions and business processes are fully aligned and contribute to the vision and strategic direction of the organisation.

This is an exciting to time to join the organisation as 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. The Data Engineering Lead will be essential to the success of this transformation.

Using your strong communication skills combined with AWS and Snowflake technical expertise, you will be responsible for managing and guiding a team of Data Engineers to develop effective and innovative solutions aligning to the organisation's architectural principles and business needs. You will ensure the team adheres to best practices in data engineering and contributes to the continuous improvement of the data systems.

Key Responsibilities:
·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.

Technical Skills Required:
·Proven Engineering Experience using the AWS Services (S3, EC2, Lambda, Glue)
·Proven Data warehousing Experience in Snowflake
·Expert in SQL and database concepts including performance tuning and optimisation
·Solid understanding of data warehousing principles, data modelling practice,
·Excellent knowledge of creation and maintenance of data pipelines - ETL Tools (e.g. Apache Airflow) and Streaming processing tools (e.g. Kinesis)
·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 priorities

Additional Experience:
·Experienced and confident leadership of data engineering activities (essential)
·Expert in data engineering practice 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 are seeking a fresh challenge to lead and take ownership of an exciting data engineering transformation project, then get in touch to find out more!

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)

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Quantum-Enhanced AI in Data Engineering: Reshaping the Big Data Pipeline

Data engineering has become an indispensable pillar of the modern technology ecosystem. As companies gather massive troves of data—often measured in petabytes—the importance of robust, scalable data pipelines cannot be overstated. From ingestion and storage to transformation and analysis, data engineers stand at the forefront of delivering reliable data for analytics, machine learning, and critical business decisions. Simultaneously, the field of Artificial Intelligence (AI) has undergone a revolution, transitioning from niche research projects to a foundational tool for everything from predictive maintenance and fraud detection to customer experience personalisation. Yet as AI models grow in complexity—think large language models with hundreds of billions of parameters—the data volumes and computational needs escalate dramatically. The industry finds itself at an inflection point: traditional computing systems may eventually hit performance ceilings, even when scaled horizontally with thousands of nodes. Enter quantum computing, a nascent yet rapidly progressing technology that leverages quantum mechanics to tackle certain computational tasks exponentially faster than classical machines. While quantum computing is still maturing, its potential to supercharge AI workflows—often referred to as quantum-enhanced AI—has piqued the curiosity of data engineers and enterprises alike. This synergy could solve some of the biggest headaches in data engineering: accelerating data transformations, enabling more efficient analytics, and even facilitating entirely new kinds of modelling once believed to be intractable. In this article, we explore: How data engineering has evolved to support AI’s insatiable appetite for high-quality, well-structured data. The fundamentals of quantum computing and why it may transform the data engineering landscape. Potential real-world applications for quantum-enhanced AI in data engineering—from data ingestion to machine learning pipeline optimisation. Emerging career paths and skill sets needed to thrive in a future where data, AI, and quantum computing intersect. Challenges, ethical considerations, and forward-looking perspectives on how this convergence might shape the data engineering domain. If you work in data engineering, are curious about quantum computing, or simply want to stay on the cutting edge of technology, read on. The next frontier of data-driven innovation may well be quantum-powered.

Data Engineering Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data. It’s the critical lifeblood of every forward-thinking organisation, fueling everything from strategic decision-making to real-time analytics. As data volumes skyrocket and technologies mature, the UK has distinguished itself as a frontrunner in data innovation. A robust venture capital scene, government-backed initiatives, and a wealth of academic talent have created fertile ground for data-centric start-ups across the country. In this Q3 2025 Investment Tracker, we’ll delve into the newly funded UK start-ups shaping the future of data engineering. More importantly, we’ll explore the rich job opportunities that have emerged alongside these funding announcements. From building scalable ETL (Extract, Transform, Load) pipelines to architecting data warehouses and implementing advanced data governance frameworks, data engineers, architects, and analysts have an incredible array of roles to pursue. If you’re eager to elevate your career in data engineering, read on for insights into the most dynamic start-ups, their fresh capital injections, and the skill sets they’re hungry for.

Portfolio Projects That Get You Hired for Data Engineering Jobs (With Real GitHub Examples)

Data is increasingly the lifeblood of businesses, driving everything from product development to customer experience. At the centre of this revolution are data engineers—professionals responsible for building robust data pipelines, architecting scalable storage solutions, and preparing data for analytics and machine learning. If you’re looking to land a role in this exciting and high-demand field, a strong CV is only part of the puzzle. You also need a compelling data engineering portfolio that shows you can roll up your sleeves and deliver real-world results. In this guide, we’ll cover: Why a data engineering portfolio is crucial for standing out in the job market. Choosing the right projects for your target data engineering roles. Real GitHub examples that demonstrate best practices in data pipeline creation, cloud deployments, and more. Actionable project ideas you can start right now, from building ETL pipelines to implementing real-time streaming solutions. Best practices for structuring your GitHub repositories and showcasing your work effectively. By the end, you’ll know exactly how to build and present a portfolio that resonates with hiring managers—and when you’re ready to take the next step, don’t forget to upload your CV on DataEngineeringJobs.co.uk. Our platform connects top data engineering talent with companies that need your skills, ensuring your portfolio gets the attention it deserves.