Full Stack Data Engineer

• Key Responsibilities:
• Design, develop, and maintain data pipelines for collecting, transforming, and loading data into various data stores.
• Build and maintain data warehousing and data lake solutions
• Develop and deploy data models that support various business requirements
• Write efficient and scalable code in languages such as Python, Scala, or Java
• Lead the design of data solutions with quality, automation, and performance in mind
• Own the data pipelines feeding into the Data Platform ensuring they are reliable and scalable
• Ensure data is available in a fit-for-purpose and timely manner for business and analytics consumption
• Work with Data Governance team to ensure solutions are compliant with regulations such as GDPR and CISO policies and data quality is baked-in to pipelines
• Maintain and optimise existing data pipelines to improve performance and quality, minimising impacts to business
• Collaborate with cross-functional teams to understand data requirements and provide support for data-driven initiatives
• Set and embed standards for systems and solutions, and share knowledge to keep the team engaged and skilled in the latest technology
• Prototype and adopt new approaches, driving innovation into the solutions
• Work closely with the Data Product Manager to support alignment of requirements and sources of data from line of business systems and other endpoints
• Effectively communicate plans and progress to both technical and non-technical stakeholders
• Develop and implement the Data roadmap for strategic data sets
• Communicate complex solutions in a clear and understandable way to both experts and non-experts
• Mentor and guide junior members of the team to help them get up to speed in a short amount of time
• Interact with stakeholders and clients to understand their data requirements and provide solutions
• Stay up-to-date with industry trends and technology advancements in data engineering
• Promote the Data Platform and Data & Analytics team brand throughout the business and represent the interests of data engineering in cross-functional forums
• Champion the importance of modern data solutions across the business and support the education of colleagues on the business value of obtaining good quality data

Knowledge, skills and experience required

• Extensive experience leading AWS and cloud data platform transformations
• Proven track record of delivering large-scale data and analytical solutions in a cloud environment
• Hands-on experience with end-to-end data pipeline implementation on AWS, including data preparation, extraction, transformation & loading, normalization, aggregation, warehousing, data lakes, and data governance
• Expertise in developing Data Warehouses
• In-depth understanding of modern data architecture such as Data Lake, Data Warehouse, Lakehouse, and Data Mesh
• Strong knowledge of data architecture and data modelling practices
• Cost-effective management of data pipelines
• Familiarity with CI/CD driven data pipeline and infrastructure
• Agile delivery approach using Scrum and Kanban methodologies
• Ability to scope, estimate, and deliver committed work within deadlines, both independently and as part of an agile team
• Supporting QA and user acceptance testing processes
• Innovative problem-solving skills and ability to provide clear recommendations
• Understanding of the impact of changes to business rules on data processes
• Excellent communication and influencing skills, especially in regard to data solutions and outcomes
• Experience managing and leading a small team of data engineers
• Self-driven and constantly seeking opportunities to improve data processes
• Strong knowledge of how data drives analytical and reporting activities, including automated marketing and personalization capabilities

Python, PySpark, SQL, NoSQL DBs (Mongo), Bash Scripting, Snowflake, Kafka, Nifi, Glue, Databrew, AWS, Kinesis, Terraform, APIs, and Lakehouse