Senior Data Engineer

Senior Data Engineer

About Glue Reply:

Glue Reply is the outcome-focused Architecture specialist within the Reply Group. Through services encompassing Business Change/Transformation, Strategy & Architecture, Data exploitation and Integration, Glue Reply is able to help its clients succeed by turning strategy into tangible solutions and vision into practical outcomes. Glue Reply diagnoses the challenges and advises on the way to make real impact – enabling its clients to deliver. www.glue.reply.com  

Role Overview:

As a Senior Data Engineer at Glue Reply, you will architect and deliver modern, cloud-native data platforms for enterprise clients undergoing digital and AI-driven transformation. This is a hands-on leadership role combining deep technical expertise with client-facing consulting, where you will design scalable lakehouse and ELT-first architectures, embed best practices across governance and observability, and build production-grade data platforms that unlock value from customer, product, and operational data.

Responsibilities:

• Lead end-to-end data workstreams across enterprise data platform and transformation programmes. 


• Design and implement scalable lakehouse architectures and modern ELT-first transformation layers (e.g., dbt). 


• Translate business requirements into robust data models, pipelines, and semantic/metrics layers. 


• Establish data contracts, data quality frameworks, governance standards, and observability practices.


• Build and optimise batch and event-driven pipelines using cloud-native tooling.


• Implement CI/CD, version control, and Infrastructure-as-Code (e.g., Terraform) within analytics engineering environments. 


• Guide client roadmaps, run architecture discussions, and communicate trade-offs to technical and non-technical stakeholders. 

About the Candidate:

• 5–7+ years’ experience in data engineering or modern data platform development.


• Proven experience delivering cloud-native data platforms (AWS, GCP, or Azure). 


• Strong SQL and Python skills, with deep experience in modern data stack tooling (e.g., dbt, Airflow or Dagster, Spark, Snowflake, BigQuery, Databricks). 


• Experience designing lakehouse architectures and ELT-based transformation workflows. 


• Practical experience implementing data governance, lineage, quality, and observability frameworks. 


• Familiar with CI/CD practices and Infrastructure-as-Code in data/analytics environments. 


• Comfortable leading client-facing engagements and shaping technical direction in complex environments.