Data Engineering Lead - AWS & Snowflake

Datatech Analytics
Stanmore
2 months ago
Applications closed

Related Jobs

View all jobs

Global Data Engineering Lead, Data Engineer

Data Engineer / Back End Developer - UKIC DV

Backend Engineering Lead

Data Engineering Manager

Technical Lead Software Engineer

Data Engineer - Leading Energy Company - London

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 availableThe roleLooking 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.DutiesKey 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.SkillsExpert in SQL and database concepts including performance tuning and optimisationSolid understanding of data warehousing principles and data modelling practiceStrong 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- PythonExcellent knowledge of creation and maintenance of data pipelinesStrong problem-solving and analytical skills, with the ability to troubleshoot and resolve complex data-related issuesProficient in data integration techniques including APIs and real-time ingestionExcellent communication and collaboration skills to work effectively with cross-functional teamsCapable of building, leading, and developing a team of data engineersStrong project management skills and an ability to manage multiple projects and prioritiesExperienceExperienced 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:www.datatech.org.ukTPBN1_UKTJ

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.