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Mutt Data

Delivers ML solutions for business outcomes

Data Engineer Lead - dbt

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in ArgentinaNo salary listed

About the Job

Requirements

  • Experience in data engineering, including building and optimizing data pipelines.
  • Strong knowledge of SQL and Python, including Pandas, NumPy, and Jupyter.
  • Advanced English proficiency.
  • Experience working with dbt for data transformation and cloud platforms such as AWS.
  • Basic knowledge of Docker.
  • Experience with orchestration tools such as Airflow or Prefect.
  • Familiarity with a business intelligence visualization tool such as Looker or Tableau.
  • Ability to mentor junior team members.

Responsibilities

  • Partner with cross-functional teams to define business intelligence and analytics requirements.
  • Innovate with new tools to improve the company's infrastructure and processes.
  • Design dimensional data models and build scalable data pipelines.
  • Develop data models to feed dashboards for teams across the organization and client-facing analytics.
  • Develop tools for the team and assist with technology migrations and model design.
  • Build scalable, high-performance data architectures.
  • Review, document, test, and integrate code through continuous integration and continuous delivery processes.
  • Lead complex projects end-to-end, delivering launches smoothly.
  • Support creation of internal and external reporting metrics.
  • Mentor junior team members.

Desired Qualifications

  • Familiarity with modern data stack tools, including Snowflake.

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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