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Job Description

Lead Data Engineer

Lead Data Engineer required to join my Client's Technology Team and contribute to their Group Architecture Board in building the next of their eCommerce data platform. You will play a pivotal role in designing and maintaining a modern, cloud-based data architecture. Working closely with product, technology, and wider business stakeholders, you will be crucial in developing scalable data pipelines, improving reliability, and enabling smarter, data-driven insights and decisions.

You will have a direct voice in how we design, build, and evolve our platform and the technologies we adopt!

We are looking for a pragmatic, innovative, and self-motivated problem solver who delivers results through collaboration and technical excellence.

Data Architecture Specialist: Design and develop robust data models, ETL/ELT processes, and data marts that support complex analytics and operational use cases.

Pipeline Optimization: Maintain and optimize data pipelines and warehouse environments to ensure top-tier performance, reliability, and scalability.

Cross-Functional Collaboration: Effectively collaborate with cross-functional teams to translate business and technical requirements into high-quality, robust data solutions.

Data Quality Ownership: Perform deep data analysis to validate assumptions, identify issues, and own data quality, consistency, and accuracy end-to-end.

Best Practices Champion: Confidently promote best practices for data engineering, governance, and platform reliability within a modern cloud-based architecture.

You will need to have skills and experience in the following:

Cloud Data Architecture: Strong knowledge of modern, cloud-based data architecture and tooling (eg, S3, Glue, Redshift, Athena, Lake Formation, Iceberg/Delta).

AWS Platform Build: Demonstrable experience designing and building modern data platforms in AWS.

ETL/Orchestration Expertise: Expertise in ETL/ELT design and data orchestration, specifically with Apache Airflow.

SQL Mastery: Strong SQL skills with significant experience in query tuning and performance optimisation.

Programming Proficiency: Proficiency in Python and Bash (for data processing, Scripting, and automation).

Data Streaming: Familiarity with streaming data technologies (eg, Kinesis, Kafka, or similar).

DevOps Principles: Experience with version control (git) and CI/CD for data pipelines.

BI/Visualisation: Familiarity with BI and visualisation tools such as Tableau, QuickSight, or similar.

Data Governance Tools: Experience with data cataloguing, metadata management, and data lineage tools.

Contextual Understanding: Passionate about data quality, governance, and understanding the business context behind the data.

This role is London-based and operates on a hybrid working model, requiring 2 to 3 days per week in the office. The compensation package offers a salary up to £100k plus a performance bonus and a comprehensive benefits package.

London (Hybrid)

To £100k plus bonus

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