Operational Data Quality Manager

Job Role:
The role holder will be crucial to underpinning our future success. You will be directly responsible for curating the data model that will drive our future business. You will be confident in bringing together stakeholders around complex challenges and guiding them to clear, tangible outcomes in relation to data quality, controls and importantly a central data model to feed our operation.

This role reports to the GM Operational Performance within the Operational Performance team. This team sits within the wider Business Operations function of Al Futtaim Automotive.

Job Responsibilities:

Establish a fit for purpose data architecture (in conjunction with IT) built on Azure platform
Oversee and implement upgrades to SAP data architecture and BIW (data warehouse) operations, covering financial and operational data, including S4Hana implementation and SAP Analytics Cloud
Develop significant improvements to the metadata and database schemas of the business to then subsequently operate this within an overarching data model and quality framework
Untangle the existing data structure of the business and associated architecture to ensure a clean data structure moving forward during the transition to Azure platform
Ensure all business requirements are captured and fed into the delivery programme for future data architecture builds and decision making
Assess (and run routine ongoing assessments) of our database health and capabilities, ensuring a consistent checklist and audit procedure to drive improvements to underlying data pipelines
Create and publish data architecture updates and directions (including architectural mapping) as required to keep the business informed on our underlying data landscape
Enforce the highest data quality and controls standards alongside IT (creating guidelines where absent)
Mandate and enforce data securities and safeguards relating to operational data
Create operating procedures for data flow and control (from ingest through to destruction/removal)
Oversee and manage all data processes for the operations function, working with IT to ensure common standards and ways of working
Research and recommend data acquisition and/or connection opportunities to enhance our data structure and capabilities (both internally across our businesses and with 3rd parties)
Develop and hone interface layers (as required and where they derive value) in conjunction with I
Oversee migration to cloud

Work with business stakeholders and IT to set the overarching data direction for the company from a data model and data architecture standpoint (under the guidance and steering of the GM Operational Performance)
Actively facilitate the collaboration of disparate data processing and analysis teams across the organization, bringing these groups together around a common direction, pipeline and way of working
Define and build the underlying data model and data pipelines for automotive operations (with the support and collaboration of IT)
Identify and promote (via business case) new solutions and architecture opportunities to enhance our data capabilities
Push forward our AI and ML direction, giving clear recommendations and guidance (+ value benefit) of AI and ML activities
Open up new revenue opportunities via data, working closely with our Blue Rewards loyalty team on how we seamlessly bring this data source into the automotive customer experience
Data Architecture, Security & Quality

Required Skills:
SAP stack (S4Hana and SAC) and Azure data platforms
Bachelor’s degree in IT, Data engineering, computer science
Minimum 5 years’ experience in data management
Data flow management

About the Team:
You will be working in a fast-paced and diverse environment, reporting directly to the GM – Operational Performance & Planning. This newly established function is responsible for creating and maintaining the highest operational standards across our business to create greater profitability for our business. Our remit is wide. Across the team, we work with central executive leadership and every Managing Director of our portfolio of automotive brands to deliver programmes and initiatives that have a direct impact on the experiences of our customers and our staff.
The culture within the team is dynamic and entrepreneurial. Across all of our team members we actively foster inquisitiveness, ideas and challenges to the status quo. We know that the operations of ‘traditional automotive’ is shifting and we want new joiners to make their mark and play a big role in how we achieve success in the coming years as part of a multi-billion AED global business. With this in mind, you do not need to have worked in automotive all of your career. We are much more focused on your talents, capabilities and motivation to roll up your sleeves and get things done.
If you think you are up for the challenge, can make a mark and see your ideas through to fruition, then you would be a perfect fit for the team and for our organization.

Education ,Experience and Knowledge:

A business or technical university degree is preferred ideally in the subjects of Computer Science, Data Engineering or any applicable data/solution architecture field. Those without a degree but with significant years of ‘in role’ experience across the specified criteria also considered.

Minimum 5 years’ experience in building and managing complex data landscapes
Deep understanding and experience with SAP stack (S4Hana and SAC) and Azure data platforms
Experienced in data mining to uncover patterns, anomalies and correlations in large data sets
Experience in managing Business Intelligence Warehouse (BIW) architectures
Comfortable interacting with APIs (ef JSON REST) and with standard tools in relation to API layer management
Fluent with workflow tooling (eg Github)
Impeccable data documentation skills (parameters, models, algorithms)
Knowledge to build multi-dimensional data models (in line with set data standards)
Experienced in working within Azure Cloud
Proven examples of realizing cost benefits across the data management flow (ingest, store, process, clean etc)
Comfortable with multiple data coding languages (eg Python, Java)
Comfortable with query language (SQL, GraphQL) to run queries and data manipulation
Comfortable with data modelling tools (ie ERWin, Visio) to visualize metadata and database schema