Job Summary

Overview: The Senior Data Engineer will support GGB UK s Chief Data Office by designing, building, testing, and maintaining reliable data pipelines, data models, and engineering solutions that turn source system data into governed, trusted, and reusable data assets. The role requires strong hands-on experience in SQL, Python or equivalent programming, ETL/ELT development, data warehousing, cloud data platforms, data quality controls, source-to-target mapping, orchestration, and production support. The role will work closely with Data Owners, Data Stewards, Business Analysts, Data Analysts, Product Owners, platform teams, and governance stakeholders to deliver scalable data foundations, improve data reliability, automate ingestion and transformation workflows, and enable analytics, reporting, governance, and downstream data products.

Responsibilities

- Data Pipeline Engineering: Design, build, test, deploy, and maintain scalable batch and near-real-time data pipelines across source systems, staging layers, data warehouses, lakehouses, and reporting-ready data layers.

- ETL/ELT Development and Automation: Develop reusable ingestion, transformation, cleansing, enrichment, and load routines using SQL, Python, orchestration tools, and approved enterprise data platforms.

- Data Modelling and Warehousing: Build and optimise relational, dimensional, and analytical data models that support business intelligence, data quality monitoring, governance reporting, and self-service analytics.

- Data Quality Engineering: Embed data quality checks, reconciliation controls, validation rules, exception handling, lineage checks, and automated monitoring into data pipelines to improve completeness, accuracy, consistency, timeliness, and reliability.

- Source-to-Target Mapping and Data Lineage: Interpret business requirements, source system structures, data definitions, and transformation rules to create, validate, and maintain technical mappings and lineage documentation.





- Platform Collaboration and Production Support: Work with cloud, database, DevOps, BI, analytics, and governance teams to troubleshoot pipeline failures, optimise performance, manage dependencies, support releases, and ensure stable production operations.

- Documentation and Engineering Standards: Maintain technical design documents, data dictionaries, source-to-target mappings, runbooks, control evidence, deployment notes, and reusable engineering patterns to ensure repeatability, auditability, and maintainability.

- Continuous Improvement and Optimisation: Improve pipeline performance, reduce manual effort, automate recurring data processes, optimise compute and storage usage, strengthen controls, and contribute to engineering best practices across the data ecosystem.

Qualifications

- Minimum Required Degree: Bachelors degree

- Preferred Degree: Bachelor s or Master s degree in Computer Science, Information Technology, Data Engineering, Software Engineering, Engineering, Mathematics, Statistics, Data Science, or a related quantitative or technology discipline

- Certificate(s)/Special Training: Preferred certification or training in SQL, Python, Data Engineering, ETL/ELT, Azure Data Fundamentals, Azure Data Engineer, Microsoft Fabric, Snowflake, Databricks, Apache Spark, Airflow or equivalent orchestration tools, dbt, Informatica, data quality, data governance, or cloud data

Knowledge, Skills and Ability

- Strong hands-on SQL skills, including joins, aggregations, window functions, stored procedures, query optimisation, data transformation, validation,



and troubleshooting

- Experience designing, developing, testing, deploying, and supporting ETL/ELT pipelines across databases, data warehouses, lakehouses, and cloud data platforms

- Proficiency in Python, PySpark, Scala, Java, or equivalent programming/scripting languages used for data engineering and automation

- Working knowledge of data warehousing, dimensional modelling, relational modelling, partitioning, indexing, incremental loads, and performance optimisation

- Experience with orchestration, scheduling, monitoring, logging, alerting, retry mechanisms, and failure handling for production data pipelines

- Understanding of data quality engineering, including automated checks for completeness, accuracy, consistency, validity, timeliness, uniqueness, and reconciliation

- Ability to interpret business requirements, data definitions, source-to-target mappings, transformation rules, and lineage needs and convert them into technical implementation designs

- Experience with Microsoft Fabric, Azure Data Factory, Azure Synapse, Snowflake, Databricks, dbt, Informatica, Airflow, or equivalent enterprise data engineering tools is desirable

- Valuable understanding of data governance concepts including metadata, business glossary, Critical Data Elements, data ownership, stewardship, controls, and issue management

- Ability to collaborate with Data Analysts, BI Developers, Business Analysts, Product Owners, platform teams, and business stakeholders to deliver reliable data assets

- Strong ownership mindset, attention to detail, documentation discipline, problem-solving ability, and willingness to work in a global stakeholder environment

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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