Data Engineer - Analytics / Modeling

• Location: Birmingham or London (flexible with travel to both locations)
• Job Type: Contract
• Start Date: ASAP
• Duration: Until end of March
• Hybrid Working: 2-3 days in the office, flexibility required

We are seeking a Data Engineer to join our Product Data Department. This role is crucial for establishing an environment for the adoption of DBT at scale, inclusive of CI/CD pipeline and development environment, based on AWS. The contract runs until the end of March with a POC completion deadline by the end of January.

Day-to-day of the role:

• Lead the design and implementation of AWS-based data products that leverage reusable datasets.
• Collaborate on creating scalable data solutions using a range of new and emerging technologies from the AWS platform.
• Demonstrate AWS data expertise when communicating with stakeholders and translating requirements into technical data solutions.
• Manage both real-time and batch data pipelines using technologies such as Kafka, AWS Kinesis, Redshift, and DBT.
• Design and model data workflows from ingestion to presentation, ensuring data security, privacy, and cost-effective solutions.
• Create a showcase environment to demonstrate data engineering best practices and cost-effective solutions on AWS.
• Build a framework suitable for stakeholders with low data fluency, enabling easy access to data insights and facilitating informed decision-making.

Required Skills & Qualifications:

• Expertise in the full data lifecycle: project setup, data pipeline design, data modelling and serving, testing, deployment, monitoring, and maintenance.
• Strong data architecture background in cloud-based architectures (SaaS, PaaS, IaaS).
• Proven engineering skills with experience in Python, SQL, Spark, and DBT, or similar frameworks for large-scale data processing.
• Deep knowledge of AWS services relevant to data engineering, including AWS Glue, AWS EMR, Amazon S3, Redshift.
• Experience with Infrastructure-as-Code (IaC) using Terraform or AWS CloudFormation.
• Proven ability to design and optimize data models to address data quality and performance issues.
• Excellent communication and collaboration skills to work effectively with stakeholders across various teams.
• Ability to create user-friendly data interfaces and visualizations that cater to stakeholders with varying levels of data literacy.