Data Engineer - Analytics / Modelling
- 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.