Technical Project Manager – Data Platforms, AWS, AI Enablement

About the role

We are seeking a Technical Project Manager to join our engineering team and drive delivery of data‑centric products, including a Data Fabric, Data Portal, Metadata services and AI‑enabled agents. The role is hybrid and focuses on organizing work in Jira, clarifying priorities and ensuring timely delivery.

Key responsibilities

• Own and maintain the team’s Jira structure, backlog, sprint board, epics, stories, dependencies and delivery tracking.
• Collaborate with engineers, technical leads, architects, product owners and business stakeholders to define scope and priorities.
• Break down engineering work into clear, sequenced tasks and monitor progress against committed timelines.
• Run delivery rituals such as sprint planning, backlog grooming, daily stand‑ups, RAID tracking and status updates.
• Identify and track blockers, risks, dependencies and decisions across Data Products, Data Portal, Metadata and Agent‑related work.
• Translate business or product requests into concrete engineering deliverables and acceptance criteria.
• Keep leadership informed on progress, risks, trade‑offs and priority conflicts.
• Drive accountability and follow‑through without being the technical decision‑maker.

Required profile

• 8+ years of experience as a Technical or Delivery Project Manager working directly with engineering teams.
• Strong background in Agile delivery and Jira administration.
• Working knowledge of AWS and cloud‑based data platforms.
• Familiarity with Data Fabric, Data Products, Metadata, Data Catalog and Data Portal concepts.
• Basic understanding of AI agents, large language models, retrieval‑augmented generation, semantic layers or knowledge graphs.
• Ability to create structure in ambiguous environments and keep teams aligned on priorities.

Required skills

• Jira
• Agile delivery methodologies
• AWS
• Cloud data platforms
• Data Fabric
• Data Products
• Metadata management
• Data Catalog
• Data Portal
• AI agents
• Large Language Models (LLMs)
• Retrieval‑Augmented Generation (RAG)
• Semantic Layers
• Knowledge Graphs