Data Product Owner

The primary purpose of the DPO role is to serve as the linchpin between the overarching data strategy. The DPO will lead the charge in shaping our data products into essential tools that are tightly aligned with our business goals. Their mission is to bridge the gap between data capabilities and the needs of the business transforming data into a powerful driver of informed decisionmaking innovation and competitive advantage.

The core focus of the DPO role is to define product roadmaps and prioritise features that directly support business objectives. Championing data quality and governance they will vigilantly monitor delivery performance and actively gather user feedback to fuel continuous improvement. The role includes the pivotal task of articulating and communicating the value of our data products to stakeholders and by doing so it will
play a central role in reshaping how we leverage data to drive growth and success.

Tasks

The DPO role will be accountable for the overall delivery of the Data and Analytics Product Team. The role will work in collaboration with their team to deliver the product backlog following the roadmap they own and have been instrumental in defining.

The DPO role will have overall line management responsibility for the D&I team. The key accountabilities of the DPO extend to effective leadership talent development resource allocation collaboration quality assurance project management technical guidance mentorship conflict resolution and performance evaluation. These responsibilities collectively enable the DPO to guide the team in achieving data product objectives and fostering a collaborative and productive work environment.
The role holder will at times need to work with external data/delivery partners and suppliers and will be confident with the requirements and demands that this necessitates. The key accountabilities of the DPO role are centred in guiding the development and management of data products to ensure they align with business objectives and deliver value.

Some of these accountabilities are:

Product Vision: Defining and communicating a clear and compelling vision for data products that align with the data and analytics strategy and broader strategic company goals.

Product Roadmap: Creating and maintaining a product roadmap that outlines the evolution of data products over time and prioritises features and enhancements based on business value impact and feasibility. Collaborating with the Scrum Master to ensure an achievable resource allocation between ongoing operational activities and new development work.

Stakeholder Engagement: Building and managing relationships with numerous stakeholders including HoDs and SMEs to ensure products meet their needs and expectations. In doing so manage expectations gain their trust and build their confidence by ensuring that the product team can deliver the required outcomes and meet an agreed level of service.

Business Impact: Demonstrating how data products contribute to achieving business goals enhancing decisionmaking and driving business outcomes.

Innovation: Challenge current ways of working and identify opportunities for innovation and new data product development to drive business growth and competitiveness.

Value Measurement: Defining and tracking key performance indicators (KPIs) to measure the value and impact of data products and demonstrating the return on investment (ROI).

Monitoring and Feedback: Continuously monitoring the benefits and usage of the data products and gathering feedback from users for improvements.

Communication and Education: Effectively communicating the value and benefits of the data products to both technical and nontechnical stakeholders and educating users on their effective use.

Risk Management: Identifying and mitigating potential risks and issues related to data products ensuring data security compliance and reliability.

Data Quality and Governance: Assist in ensuring that data used in products is of high quality consistent and compliant with data governance and privacy regulations.

Operational Adoption: Work collaboratively with stakeholders to embed data products successfully into operational teams to maximise their use and ROI.

Requirements

• Experience of dealing with recording metadata and other music industry related data sets
• Knowledge of relevant programming languages and tools (SQL & Python).
• Experience of working with cloudbased data platforms (AWS) and Databases (Snowflake)
• Experience of working with advanced analytics solutions such as machine learning
• Familiarity with regulatory requirements and compliance in data management especially in industries with strict data regulations.
• Strong understanding of data quality data governance and data privacy best practices.
• Data Governance: Understanding of data governance principles and practices to ensure data quality and compliance.
• Machine Learning: Awareness of machine learning and artificial intelligence concepts as they relate to data product development.