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Mutt Data

Delivers ML solutions for business outcomes

Data Governance & Platform Lead

Data ManagementConfirmed live in the last 24 hours
Full-timeBuenos Aires, ArgentinaNo salary listed

About the Job

Requirements

  • Solid experience as a Data Engineer, Tech Lead, or Data Architect, with the ability to lead technically and coordinate with different teams.
  • Experience with AWS, with priority given to this experience.
  • Experience with dbt, with priority given to this experience; dbt is the main tool for the data catalog.
  • Experience going through a data governance implementation cycle, ideally in banking.
  • Experience leading data governance initiatives or projects for more than one year, not only using tools as a team member.
  • Experience implementing tools.
  • Knowledge of data platforms and data catalog, data quality, or data observability tools.
  • Ability to communicate with technical and functional profiles and bring a cross-cutting perspective.

Responsibilities

  • Work with the data governance, architecture, and platform teams while understanding business needs.
  • Actively participate in technical decisions around data catalog, data quality, and data observability.
  • Bring practical implementation criteria by identifying dependencies, proposing courses of action, and helping drive them forward.
  • Help resolve technical definitions and unblock progress.
  • Support the evolution of prioritized data products.

Desired Qualifications

  • Experience with Elementary, DataHub, Apache Atlas, or similar tools.

About the Company

Mutt Data

Delivers ML solutions for business outcomes

Mutt Data helps startups and large enterprises build and implement machine learning solutions that deliver real business results. They work across finance, insurance, advertising, telecoms, on-demand services, and e-commerce to help clients get ahead using the latest methods and best practices. How it works: They provide end-to-end ML services, starting with understanding business goals, data assessment, data engineering, model development, deployment, and ongoing monitoring and optimization. They tailor tools and techniques to each industry to put models into production and track their impact. Differences: They serve both startups and big companies and offer a complete workflow from idea to production, across many industries, with a focus on practical business outcomes rather than research-only efforts. Goal: Help customers gain a competitive edge by turning data into deployable ML solutions that improve efficiency, revenue, and user experience.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Capital, Argentina
Founded
2017

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