Associate Director - Data Product Manager

Title - Associate Director - Data Product Manager

GCL- E

Typical Accountabilities:

• The role holder will execute the following accountabilities autonomously with limited supervisory oversight:

• Works with consumers / business users on the definition of the data requirements for intended data solutions.

• Able to translate unstructured, complex business problems into a data design and solution

• Profiling of data to understand provenance, quality, metadata models, ownership and compliance to internal and external regulatory standards

• Ad hoc wrangling of data (sourcing, extraction, profiling, integration) to support Data Science model generation and business insight

• Support of data engineers in the development of Source to Target pipelines (e.g. ETL design)

• Design & testing of the quality and performance of derivative data models in reporting and analytics solutions

• Processing of requests for compliant access to data

• Defining and managing information lifecycle management in data solutions

• Provision of data understanding (structure, provenance, quality) to Architects, Data Engineers and Data Scientists to support use in Analytics projects.

• Supports IT and business data teams in identifying and managing Critical Data Assets and Elements including Reference, Master and Metadata.

• Collaborates with Risk, Assurance, Privacy, Information Security and Regulatory authorities to ensure data and information controls are in place and adhered to.

• Clearly and objectively communicate insights and results, as well as their associated uncertainties and limitations

• Guidance of junior Data Analysts – supervision of task completion, support in trouble shooting challenges and contributing to performance evaluation reports

• Sharing of insight and best practice in community forums supporting capability development.

• Personal development and training in more complex data analysis skills, techniques and tooling

• Provision of domain data expertise (data standards, systems, metadata models, policies, business processes) in at least 1 domain area (e.g. chemistry, finance) and will be developing expertise in further domains

• Working with senior personnel they will contribute to

• Development of best practice for Data Analysis: Methods and Technology: technology evaluation, POCs

• Provision of training and skill development in the best practice of Data Analysis: training materials, FAQs, Playbooks and integrated operating models.

•

• 3 key specialisms include:

• Source Data Analysts: Support engineers build/configure source applications by defining the data requirements and modeling the appropriate data structures for given use cases. They define data quality criteria to ensure data quality integrity of the application, develop logical data models (compliant to any RMDM standards), ensure that the project deliverable aligns with the logical design and business requirements (requirements traceability).

• Integration Data Analyst: Support engineers build composite analytics applications by defining data requirements, data structures and data integration paths. They will identify, profile and quality assess potential source data sets, understand and comply with any data restrictions (e.g. GDPR, License, IDAP controls, etc), develop integration patterns (ETL design), support the design of target data models (compliant to any MDM standards) and document to support re-use and management of the application.

• Data Steward: defining and managing data governance policies, standard and operating processes; the facilitation and operation of data and information governance activities; data quality issue management; the establishment and operation of governance controls including data access, lifecycle and metadata management; risk based approach to remediation and mitigation planning.


Typical People Management Responsibility (direct / indirect reports):
• Approximate number of people managed in total (all levels) - 2-3
• Manager of a team
• Matrix Manager – (projects/dotted line)

What is the global remit? (how many countries will the role operate in?):
• Another country

Education, Qualifications, Skills and Experience:
• Essential: Undergraduate degree in a Computer Science, Data Management or possibly discipline area (R&D, Finance, HR etc) and cross trained or equivalent number of years of experience; Proven experience in a data analyst or business role aligned to data and information management role with practical examples of performing data analysis in terms of defining requirements, gleaning critical data elements, defining data quality criteria and checkpoints; Domain data understanding: the structure, provenance and meaning of the source data crucial to the domain (eg. SAP for Finance, SDTM for Clinical). Understanding of the business processes in the generation and consumption of data
• Desirable: Post-graduate degree in MIS, Data Management

Skills and Capabilities:
• Essential: The role holder will possess a blend of data requirement analysis, data quality analysis, data stewardship skills; Experience in translating requirements into fit for purpose data models, data processing designs and data profile reportsExperience in the use of data modeling technologies; Experience in working in multi-skilled, multi-location data teams, working to agile principles.; Knowledge of key AZ policies and standards for data covering areas such as privacy and security.; Excellent written and verbal communication, and consultancy skills; Awareness of the end to end processes and activities in the build and support of Data solutions; Experienced in applying a risk based methodology to data and information management; Experience in the use of metadata cataloguing tools; Experience of Data Analysis enabling tool kits
• Desirable: Leading the work of others – task setting, supervision and coaching of more junior staff

Key Relationship to reach solutions:
• Internal (to AZ or team): Working with peers and team leaders in the business and IT in the delivery of data capabilities; Junior data analysts in supervising delivery; Data engineering teams to deliver data structures and data provisioning processes; Data Science teams supporting ad hoc data access and provision; Key assurance teams including Risk, Privacy Information security and audit; Other data analysts across AZ to develop and extend data analysis approaches and best practices
• External (to AZ): Outsource partners to deliver and support data structures and data provisioning processes