Data Engineer - Daresbury
Role Description
As a Data Engineer will be a core member of the wider technical team, responsible for designing, building, and optimising the pipelines that turn raw network, commercial, and operational data into trustworthy, analytics-ready assets. Working closely with Data Analysts, Network Engineering, and Digital teams, you will own our data platform, ensuring data is accurate, timely, and secure to drive reporting, automation, and develop towards AI-driven decision-making.
Key Responsibilities
• Design, develop and maintain scalable ELT/ETL pipelines that ingest data from network devices (IPDR, SNMP, OSS/BSS), CRM, finance, and IoT platforms into our BI warehousing environment.
• Model data using Kimball/star schemas and data-vault principles to support BI and self-service analytics.
• Implement data quality, lineage, and observability tooling (e.g., dbt tests, Great Expectations, Azure Purview).
• Optimise storage and compute costs through partitioning, incremental loads, and automation.
• Collaborate with DevOps to embed CI/CD and Infrastructure-as-Code (Terraform, Azure DevOps pipelines).
• Work with stakeholders to capture data requirements and translate them into technical solutions.
• Ensure data is handled in line with GDPR, ISO-27001, and company security policies. • Mentor junior data team members and champion a data-driven culture across ITS.
About You
• Proven experience as a Data Engineer (or similar), building cloud data platforms.
• Strong SQL and Python skills for data transformation and automation.
• Hands-on with modern ELT frameworks (e.g., dbt, Fivetran, ADF, Synapse Pipelines). • Experience with version control (Git) and CI/CD for data.
• Knowledge of data modelling techniques and performance optimisation.
• Familiarity with data governance, security, and privacy best practices.
Desirable
• Experience with network-centric datasets (fiber, GPON, ethernet, Wi-Fi telemetry).
• Exposure to streaming technologies (Kafka, Event Hubs) and real-time analytics.
• Knowledge of Machine Learning Ops (MLflow, Databricks)