AWS Data Engineer
Salary: £50,000 - £80,000 - Bonus + Pension + Private Healthcare
Location: London / UK Wide Location - Hybrid working
* To be successfully appointed to this role, you must be eligible for Security Check (SC) and/or Developed Vetting (DV) clearance.
The Client:
83zero is proud to be partnered with a global leader in digital services, driving innovation in customer experience through CRM, marketing, business intelligence, and cloud solutions. Their cutting-edge technologies are tailored for enterprise clients, delivering platforms that not only meet today's business needs but also pave the way for future growth. These solutions empower digital transformation initiatives, unlock new business opportunities, and make customer relationship operations more relevant in today's evolving landscape.
Hybrid Working:
Your work locations will vary based on your role, business needs, and personal preferences. This will include a mix of office-based work, client sites, and home working, with the understanding that 100% home working is not an option.
Your Role:
• Create robust pipelines to ingest, process, and transform data, ensuring it is ready for analytics and reporting.
• Develop, Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) workflows to seamlessly move data from source systems to Data Warehouses, Data Lakes, and Lake Houses using Open Source and AWS tools.
• Utilise DevOps methodologies and tools for continuous integration and deployment (CI/CD), infrastructure as code (IaC), and automation to streamline and enhance our data engineering processes.
• Leverage your analytical skills to design innovative data solutions that address complex business requirements and drive decision-making.
Your Skills and Experience:
• Demonstrable experience using AWS Glue, AWS Lambda, Amazon Kinesis, Amazon EMR, Amazon Athena, Amazon DynamoDB, Amazon Cloudwatch, Amazon SNS and AWS Step Functions.
• Strong experience with modern programming languages such as Python, Java, and Scala.
• In-depth knowledge of Data Warehouse, Database technologies, and Big Data Eco-system technologies such as AWS Redshift, AWS RDS, and Hadoop.
• Proven experience working with AWS data lakes on AWS S3 to store and process both structured and unstructured data sets.
To apply please click the "Apply" button and follow the instructions.
For a further discussion, please contact Caitlin Earnshaw.
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