Big Data Project Manager

Role Purpose:

We are undertaking a Data Transformation and as data becomes fundamental to what we are doing, the ability to have the right data in the right place at the right time, that is accurate and consistent is essential.

This role will play a really important central role within this evolving landscape, working alongside the Technology Team and the Product Owner to orchestrate and facilitate good delivery of data to the business. You will work alongside the Product Management, Product Owner, Scrum Master, Stakeholders, and assist them by listening to the business and challenging the Product owner for continuous improvement of processes and delivery of significant data projects within the Technology area.

You will have to be competent in communicating to the Business as well as technical teams to capture requirements for new data and solutions and translate them into the technology team to make sure these requirements are accurately developed into technical solutions that can be developed, tested, integrated and documented. Meanwhile able to lead and orchestrate the transformation from end-to-end.

 

Responsibilities:

• Working with the relevant business Financial Directors and Business Stakeholders to understand the business logic, and translate this into requirements that can be fed into the technical team to build a solution.
• Translating requirements from simple business logic into technical requirements for the Technology teams.
• Working closely with the business stakeholders and Solution Architects you will ensure original scope is documented and delivered accordingly in a timely manner.
• Liaise with sprint managers and QA leads as per framework to ensure delivery is efficient and in line with business expectations.
• Proactively drive process improvement across the Business facing side of the Technology Team.
• Sharing knowledge with the wider business, working with other BA’s and technology teams to make sure processes and ways of working is documented, and encourage continuous improvements.
• Take lead on the communication to the business, on status of items and blockers.
• Have an end to end knowledge of the data landscape and be able to shape future technological decisions.
• Lead and own the rollout of high-scale data implementations and projects.
• Manage business stakeholder relations and expectations.

Skills & Experience:

• Strong track record of business analysis within a technical environment.
• Strong knowledge of Data Warehousing architecture, desirable to have Azure experience.
• Experience working in high-priority data projects.
• Extensive expertise in breaking down problems/issues, documenting clearly in a format for stakeholders to easily understand and prioritise.
• Stakeholder Management.
• Able to collaborate with a wide range of technical and non-technical colleagues and stakeholders including development teams and BA’s.
• Working in an agile environment and able to effectively work in a fast paced organisation.
• Significant understanding of Lean methodologies, and to be able to design processes in such manner.
• Strong experience of transformation management and continuous improvement.
• Strong experience in managing and orchestrating the delivery of big data projects.
• Having worked in SAFE teams previously will be an advantage.
• Good awareness of Product Management and Product Owner responsibilities.
• Communication is key with the ability to adjust the story based on the context.
• Ability to breakdown user stories and create design documents for development.

Behavioural Fit:

• Highly technical with a keen eye for detail.
• Driven, self motivated and results oriented.
• Confident and an ability to challenge if necessary.
• Structured and organised.
• Ability to work in a cross-functional, multi-cultural team and in a collaborative environment with minimal supervision.
• Ability to multi-task and plan, organize and prioritize multiple projects.
• Must have a hands-on mentality.

Role Key Performance Indicators:

• Quality and consistency of data across the whole data landscape
• Time to value of new data and analytics solutions including implementation of Machine Learning
• Quality of documentation and time to get up to speed of new joiners.