Senior Tech Product Manager Data

The Senior Data Product Manager is one of the two core leadership roles in ASOSs new Tech & Digital
Product Operating Model working together with the Engineering Lead to drive product success.
This role focuses on understanding business and customer needs defining product vision and
ensuring the delivery of data and software products that align with strategic business goals.
Product Managers are responsible for defining the why and what behind product development
while their engineering counterparts focus on the how and when. Together they ensure the
team delivers valuable high-quality solutions that meet both business and customer needs.

Data Product Managers typically work with a cross-functional team and work closely with key
stakeholders including engineers product development teams and other business leaders to
define data product requirements and set priorities. They focus on solving the right problems
with innovative solutions that evolve our data platform and build data products that deliver
business value.

As a Senior Data Product Manager for Platform Foundations you will define the vision strategy
and roadmap for ASOSs data ecosystem. You will ensure the development of a scalable
secure and robust data infrastructure that aligns with ASOSs strategic goals. By partnering with
engineering leads and other stakeholders you will enable teams to access high-quality reliable
and governed data. You will foster advanced analytics AI and data product development. Your
role will drive data ownership continuous improvement in data infrastructure and governance
ensuring high standards of data quality governance and compliance. This position is crucial for
supporting innovation scalability and data-driven decision-making within ASOS.

Key Responsibilities

Product Leadership

• Product Vision & Strategy:
  Define and communicate a compelling product vision and roadmap for ASOSs data ecosystem aligned with ASOSs strategic business goals and customer needs. Clearly articulate the why behind the product offering and prioritise features that maximize value.
• Problem Definition:
  Work with stakeholders to deeply understand your customer value/user pain points identify data champions seek out opportunities and translate them into actionable product and platform requirements.
• Roadmap Development:
  Create and maintain the data product roadmap that aligns with business goals customer needs and technology trends.
• Prioritisation:
  Prioritise product features and enhancements based on customer feedback business impact and technical feasibility in collaboration with engineering teams.

Product Management

• Frameworks and Methodologies:
  Implement relevant methodologies to ensure iterative development continuous feedback and rapid delivery of product features.
• Innovation & Problem-Solving:
  Explore new data capabilities and initiate proof of concept(s) and experiments to build innovative data solutions that push the boundaries of data products and platforms.
• Continuous Improvement:
  Foster a culture of continuous improvement by regularly reviewing processes and outcomes to enhance product development efficiency.

Customer Feedback Integration

• User Research:
  Conduct user research and gather customer insights to inform product decisions and identify areas for improvement.
• Continuous Improvement:
  Continuously refine the data product roadmap based on customer feedback data quality reviews data definitions business priorities and tech opportunities. Ensure that the team learns and iterates rapidly to build robust data products.
• Customer-Centric Approach:
  Serve as the voice of the customer within the team ensuring that all decisions are grounded in delivering the best possible experience for users.

KPI Setting and Tracking

• KPIs & Success Metrics:
  Define own and track key performance indicators (KPIs) to measure product success ensuring that each feature delivered drives tangible business outcomes.
• Reporting:
  Provide regular updates and reports on product performance to stakeholders highlighting successes and areas for improvement.
• Data-Driven Decision Making:
  Use data to inform product decisions leveraging customer insights analytics and experimentation to validate assumptions and prioritize the right features.

Collaboration with Engineering Leads

• Strategic Alignment:
  Partner with engineering leads to ensure product strategies are technically feasible and aligned with engineering capabilities.
• Shared Accountability:
  Collaborate in planning sessions sprint ceremonies and other key activities to…