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Mutt Data

Delivers ML solutions for business outcomes

Data Analytics Senior

Business IntelligenceConfirmed live in the last 24 hours
Full-timeRemote in ArgentinaNo salary listed

About the Job

Requirements

  • At least 5 years of experience building production data pipelines.
  • Strong experience with PySpark, Delta Lake, and Medallion architectures.
  • Hands-on experience with Microsoft Fabric, including Lakehouses, Notebooks, Data Pipelines, and OneLake.
  • Strong SQL skills, including common table expressions, window functions, and analytical queries.
  • Experience building reliable, production-grade pipelines, including backfills, reruns, monitoring, and handling late-arriving data.
  • Familiarity with the Azure ecosystem, including Blob Storage, Entra ID, and Key Vault.
  • Advanced English proficiency to collaborate effectively in distributed teams.

Responsibilities

  • Build and maintain interactive dashboards and reports in Power BI to support business and operational decision-making.
  • Develop and optimize data models, datasets, and semantic layers using Databricks and SQL.
  • Design, validate, and automate data transformations to ensure high-quality, reliable reporting.
  • Collaborate with Data Engineering and business stakeholders to define reporting requirements and key performance indicators.
  • Monitor data quality, troubleshoot reporting issues, and ensure consistency across dashboards and data sources.
  • Create and maintain documentation for dashboards, metrics, and reporting processes to support scalability and knowledge sharing.

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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