Engage proactively with internal business units (IT, Finance, Technical Operations, Software) to surface, qualify, and document AI and data use cases.
Manage and maintain a prioritised use case backlog, balancing business value, technical feasibility, and data readiness.
Conduct structured discovery workshops and stakeholder interviews to translate operational pain points into clearly scoped data opportunities.
Customer-Facing Enablement
Partner with Customer Success and the Product team to identify data and AI capabilities that enhance the Speedcast customer portal experience.
Gather and translate external customer feedback into structured requirements for portal-facing analytics, dashboards, and AI-assisted features.
Act as the internal voice of the customer when prioritising data platform investments that drive customer-visible outcomes.
Execution & Delivery Oversight
For use cases within the data lakehouse ecosystem, act as the primary liaison with Data Engineering to scope, deliver, and validate solutions across platforms including Dremio, Confluent Kafka, TimescaleDB, and AWS.
Lead or co-lead delivery of workflow and process-oriented use cases directly, coordinating cross-functional resources as needed.
Define acceptance criteria, track delivery progress, and communicate outcomes clearly to business stakeholders.
Training & Organisational Enablement
Design and deliver training programmes, workshops, and self-service resources that build AI and data fluency across non-technical teams.
Develop training content covering prompt engineering fundamentals, data literacy, BI tool adoption, AI assistant usage, and responsible AI practices.
Measure adoption and learning outcomes, iterating on content and delivery methods based on feedback.
Governance, Standards & Communication
Collaborate with IT, legal, and data leadership to ensure use cases comply with data governance, privacy, and security standards.
Serve as the face of the AI and Data capability to the business, managing stakeholder expectations and communicating progress at all organisational levels.
Develop and maintain artefacts including use case documentation, roadmaps, training materials, and executive status reports.
Working at Speedcast:
Find great opportunities to make an impact. We have a "one team, one dream" mentality. We work together to make great things happen. Working at Speedcast isn't just a job, it's a career that you can take to new levels.
Top reasons why people love working at Speedcast:
Our global presence - you get to work with clients and colleagues all over the world, in every continent
Talented teammates - your co-workers are the best and brightest in the industry
Industry leadership - be part
Benefits
Remote work options
Additional Information
AI Enablement Manager
Job Title: AI Enablement Manager
Location: Remote, Spain
Salary: EUR 75,000 - 85,000 per annum (depending on experience)
Overview of Position:
The AI Enablement Manager is a strategic and execution-oriented role responsible for identifying, qualifying, and driving AI and data use cases across Speedcast. Sitting at the intersection of business stakeholder engagement, data platform capability, and organizational change, this role serves both internal teams - including IT, Finance, Technical Operations, and Software Engineering - and external customers consuming Speedcast services through the customer portal.
A successful candidate will combine business consulting instincts with a practical understanding of modern data platforms, enabling them to translate ambiguous business problems into structured, actionable data solutions. Where use cases require the data lakehouse ecosystem (Dremio, Kafka, TimescaleDB, AWS), this role acts as the primary liaison with the Data Engineering team. For workflow and process-oriented use cases, the role leads delivery directly. Across all tracks, the AI & Data Enablement Manager owns a training and upskilling programme that builds AI and data literacy organisation-wide.
This is a highly visible role that will grow in scope as Speedcast expands its AI and data capabilities. The ideal candidate is equally comfortable presenting a use case pipeline to executives as they are running a workshop with a field operations team or reviewing requirements with a software engineer.