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Data Engineering Lead - AWS & Snowflake


Job details
  • DataTech Analytics
  • 2 months ago
Applications closed

Description

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: J12869

Full UK working rights required/no sponsorship available

THE ROLE
Looking for a challenge in one of the world's 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.

DUTIES
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.

PERSON SPECIFICATION
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
oAWS services (S3, EC2, Lambda, Glue)
oETL Tools (e.g. Apache Airflow)
oStreaming processing tools (e.g. Kinesis)
oSnowflake
oPython
• 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 priorities
Experience
• 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.

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