Data Architect - Stockholm

Veritaz is a fast-growing IT-consultant firm. Our company is made up of insanely bright people from over 4 countries, and we are located in Sweden, UK, US and Pakistan. The voyage has been incredible this far, but it is only the beginning.
Assignment Description:
We are looking for a Data Architect who is experienced in working with various EA frameworks preferably with an Information architecture focus.
Do you value openness, transparency, and empowerment? Our squad is high performing cross-functional team who are set with a mission to provide win-win exchanges for our customers.
What you'll do:
● Gather data and model the content for developing high level, enterprise data models for the objects in scope.
● Oversee and assess technical and solution architecture designs, including the conceptual, physical, and logical data models.
● Close collaboration with the business/IT and functions to fully understand the needs to proactively advise in relation to the current architecture.
● Provide an enterprise-wide view and be responsible for standard development across all aspects of data, information, and high-level designs.
● Hands on work to create logical and physical data models using best practices to ensure high data quality and reduced redundancy.
Who you are:
● 5+ years of experience working as a data/information architect, data engineer, and/or BI Engineer/Developer or in a similar role.
● Good knowledge and experience from working within large organizations utilizing agile methodologies such as SAFe, Scrum, and/or Kanban.
● Have an analytical mindset with the ability and willingness to translate how best we build and deliver value as well as not being afraid to get hands-on when there is a need.
● Excellent communicator with strong networking skills, handling many different stakeholders on different levels with a good ability to inspire others and multi-task in a fast-paced environment.
● Highly knowledgeable about relevant Data Management & Governance topics including Data Glossaries, Taxonomy management, Data Cataloguing, Data Lineage, Data Quality and Automated Data Discovery with demonstrable practical experience in as many of the above as possible.