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Senior Data Architect

MMoonfare
Seniority
Senior
Model
In-Office
Sector
Fintech
Salary
Undisclosed
Contract
Full-Time

About the role

Moonfare defines the role of Data Architect as someone who can design both the data structures (data objects), as well as the data machinery (data pipes, data stores, interfaces) on and through which data objects move. Your role is critical in positioning the firm for AI and advanced analytics adoption while ensuring high standards of data quality and security.

What you'll do

  • Act as the lead authority on data architecture, ensuring seamless alignment between business objectives, data strategy, and technology solutions.
  • Design and maintain logical and physical data models, schemas, and structures specifically tailored to support reporting, analytics, and effective operational processing, utilizing both dimensional and operational warehouse modeling techniques.
  • Oversee data integration processes and recommend management tools to enhance infrastructure, ensuring the accurate and timely flow of data between systems, applications, and platforms.
  • Develop a comprehensive data strategy that supports financial business objectives and drives governance frameworks to ensure compliance with strict regulatory requirements and data privacy obligations.
  • Manage enterprise data assets, including metadata repositories and data lineage documentation, to ensure they are accessible and up to date.

What you'll need

  • Proven track record in designing and implementing enterprise data architectures within a regulated financial services environment.
  • Strong expertise in enterprise data modeling principles, with a specific background in both dimensional and operational modeling applicable to the finance sector.
  • Experience in data integration, ETL processes, and database management across multiple platforms.
  • Experience in delivering AI and advanced analytics projects from inception to production.
  • Comfortable with modern data platforms, cloud-based technologies (data lake/big data), and tools for metadata management and data lineage.
  • Ability to translate complex technical concepts into clear, actionable insights for non-technical stakeholders across the organization.
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