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The Data Operations Specialist is responsible for supporting the operational stability, maintenance, and continuous improvement of the organisation’s data and analytics platforms. This includes contributing to the reliable delivery and support of enterprise data services across technologies such as SAP BW, SAP BusinessObjects, SAP Data Services, SAP InfoSteward, Power BI, Snowflake, AWS-based data platforms, and related analytics technologies.
The role focuses on executing operational activities, resolving incidents, supporting enhancements, and ensuring data platforms perform effectively and reliably. The Data Operations Specialist works closely with the Data Operations Team Lead, as well as data engineers, architects, analysts, and business stakeholders to support business needs and maintain platform performance.
Over time, the role will contribute to the evolution toward a DataOps capability, supporting improved monitoring, automation, and operational efficiency across the data ecosystem.

Minimum Qualificaton

Bachelor's degree or diploma in Information Systems, Computer Science, Data Engineering, or equivalent combination of education and experience.

Minimum Experience
2–5 years’ experience working with enterprise data or business intelligence platforms.
Experience supporting or developing solutions in SAP BW, Snowflake, or similar data platforms.
Experience with data integration, ETL processes, and analytics tools.
Experience in an operational support or service delivery environment.
Exposure to cloud platforms (e.g., AWS) and tools such as Power BI is advantageous.
Retail industry experience is advantageous.
Knowledge of SAP Line of Business Systems (ERP, CAR, CRM) is advantageous

Key Responsibilities

Part of the Enterprise Data and Analytics team, responsible for supporting the operational maintenance and support of enterprise data platforms and analytics systems.

The role supports technologies including, but not limited to:
SAP BW
SAP BusinessObjects (BOBJ)
SAP Data Services
SAP InfoSteward
SAP Analysis for Office (AO)
Power BI
Snowflake
AWS-based data platforms and DevOps processes
Other current or future analytics and data technologies

The role contributes to maintaining operational stability and supporting the ongoing evolution toward a DataOps operating model.

Business (Stakeholder) Engagement:
Engage with business stakeholders to understand data-related issues and operational requirements.
Communicate effectively with business users and technical teams.
Provide input into solutions and communicate progress on incidents and requests.
Support the translation of business requirements into operational or technical actions.

Service Delivery and Incident Management:
Provide first- and second-line support for data platform incidents and service requests.
Investigate, analyse, and resolve data platform issues within agreed SLAs.
Perform root cause analysis and contribute to preventative actions for recurring issues.
Execute approved system changes or enhancements (e.g., SAP BW configuration).
Prepare and execute test scripts, including unit and regression testing as part of incident resolution.
Ensure accurate documentation of incidents, resolutions, and operational procedures.
Participate in standby and support rotations as required.

Collaboration:
Working well in a team.

Data Platform Operations:
Monitor system processes, batch jobs, data pipelines, and integrations.
Support the availability, stability, and performance of enterprise data platforms.
Assist in maintaining monitoring and alerting processes.
Support the operation of ETL processes, reporting environments, and analytics platforms.
Work with infrastructure, DevOps, and cloud teams to resolve platform-related issues.
Ensure alignment and consistency across SAP and non-SAP systems.

Data Ops and Continuous Improvement:
Contribute to DataOps practices, including monitoring, automation, and operational improvements.
Identify opportunities to improve operational processes and system performance.
Support improvements in deployment processes and data pipeline reliability.
Collaborate with engineering and architecture teams to enhance platform resilience.

Work Planning and Delivery Support:
Apply knowledge of the data and analytics lifecycle to support operational and enhancement activities.
Deliver assigned tasks within agreed timelines and quality standards.
Support the implementation of enhancements and operational improvements.

Data/ Information Governance:
Adhere to data governance standards, policies, and processes.
Support data quality monitoring and remediation activities.
Contribute to maintaining data definitions and metadata standards.
Ensure appropriate testing and validation of system changes.

Analytical & Conceptual Thinking:
Data Literacy: The ability to read, write and communicate data in context, including an understanding of data sources and constructs and the ability to describe the use case, the application and resulting value.
Information Literacy: The ability to know when there is a need for information; to be able to identify, locate, evaluate and effectively use that information for the issue or problem at hand.
Retail business knowledge and dynamics is essential for understanding data and information in context. The role requires the ability to keep in mind mental models of various data assets and interdependent business process relationships whenever working through analysis.

Competencies

Analytical & Problem-Solving: Strong ability to analyse complex data and system issues, identify root causes and implement effective solutions.

Data & Technical Acumen: Practical understanding of enterprise data platforms, ETL, BI, cloud technologies and data operations.

Data Literacy & Governance: Ability to understand data in business context and apply data quality, governance and information management principles.

Service Delivery: Strong customer focus with the ability to manage incidents, prioritise requests and deliver within agreed SLAs.

Stakeholder Engagement: Clear and effective communication with both technical and non-technical stakeholders.

Operational Excellence: Strong attention to detail and focus on platform stability, reliability, performance and quality.

Continuous Improvement: Identifies opportunities to improve processes, automation, monitoring and DataOps practices.

Collaboration: Works effectively across data, technology, infrastructure, DevOps and business teams.

Planning & Execution: Organised and accountable, with the ability to manage priorities and deliver quality work within agreed timelines.

Learning Agility: Adaptable and willing to learn new technologies, tools and ways of working in a continuously evolving data environment.

Closing Date: 20 October 2026

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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