ETIC, Resource Manager Operations – Senior Associate

The Resource Management Operation Specialist team delivers end-to-end analytics solutions across a wide range of internal data domains, covering  resource management, data ingestion and transformation through to dashboard and analytics consumption, with the people data domain being a key area of focus.

While end-user outputs include dashboards and ad-hoc analytics, these are underpinned by centrally managed production data pipelines and curated data assets. The team builds and maintains these centrally owned assets for use across the business, ensuring solutions are consistent, reusable, and straightforward to support over time.

As part of the Resource Management Operation Specialist, the candidate will need to be focused on back-end data engineering and data model design, contributing to the development and operation of production data pipelines and data assets.

Work is delivered primarily in Databricks using PySpark and Spark SQL, with a strong emphasis on quality, reusability, and long-term sustainability.

The role will be responsible for the following activities:

• →Resource Management Operation
• →Stakeholder management, communication, and organization
• →Designing and evolving data models and pipeline architectures that support analytics and reporting use cases.
• →Building and maintaining production-grade data pipelines in Databricks using PySpark and Spark SQL.
• →Supporting production data pipelines by investigating data defects or failures when raised, performing root cause analysis, and implementing permanent fixes.
• →Translating problem statements and reporting needs into well-structured data solutions, operating with minimal guidance.
• →Working with the internal team and, where appropriate, business stakeholders to clarify data requirements from a data model and engineering perspective.
• →Championing well-designed, consistent data models that enable reliable downstream analytics and reduce duplication.
• →Contributing to shared engineering standards through code review, reusable utilities, and common design patterns
• →Producing clear code-level documentation and contributing to shared technical documentation to support knowledge transfer and long-term maintainability.
• →Managing code in Git-based version-controlled environments.

The role requires strong, hands-on experience in:

• Resource Management
• Data engineering using Databricks, with practical experience in PySpark and Spark SQL.
• Designing data models optimised for analytics and reporting consumption.
• Building, operating, and improving production data pipelines in an enterprise environment.
• Using Git for version control, including managing branches, pull requests, and participating in code reviews.
• Writing and maintaining clear, well-structured code suitable for long-term ownership and reuse.
• Broader Python development to support data engineering workflows beyond core transformations.

Working alongside downstream analytics tools such as Power BI, with sufficient understanding to ensure data models support efficient consumption (without owning dashboard or semantic model development).