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# Senior Data Engineer

- **Company:** Reply Ltd
- **Location:** UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Python (Programming Language), Operational Data Store, Cloud Services, Standard Sql, Snowflake, Apache Spark, Data Management, Terraform, Software Version Control, Databricks
- **Published:** November 4, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/senior-data-engineer/54852179

## About the Role

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.

## Description

As a Senior Data Engineer at 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.