Analytics Engineer

2 weeks ago

Montreal, Quebec, Canada Maxa AI Full time
About The Role

At Maxa, Analytics Engineers provide clean data sets, modeling data in a way that empowers end users to answer their own questions. Analytics Engineers can also develop reports and train users on how to use data in visualizations tools.

This role requires excellent software engineering, data modeling and reporting skills.

Key Responsibilities
  • Working with customers & document business data definitions and rules;
  • Create meaningful data models;
  • Create a reliable, generic, and automated data mapping and transformation pipelines;
  • Work closely with data science, data engineering and product teams;
  • Support business teams in better understanding business data;
  • Critical thinking and analysis of quality, validity and reliability of business data.
Strong knowledge of
  • Languages : SQL, Python
  • Framework and tools : DBT
  • Data SQL database engines (Snowflake, MySQL, etc)
  • Enterprise Big Data & ERP Systems
  • Git
Be familiar with
  • Machine Learning (considered an asset)
  • Data pipelines & Distributed Data Processing (Spark, Kafka, etc.)
Soft skills
  • Customer-oriented
  • Communication, presentation, and collaborative skills to ensure quality of deliverables
  • Unconventional critical and creative thinker, and open minded
  • Develop new methodologies and contribute to the company's technical philosophy
Qualifications
  • University degree in related fields or work experience equivalent
  • Proficient in English and French
  • 5 years of experience in data modeling
  • 3 years of experience with Enterprise Big Data & ERP data
About MAXA AI

Maxa works seamlessly with ERP systems to unlock the predictive power of data, allowing business teams to make decisions faster, with confidence.

Companies rely on Maxa to turn raw ERP data into predictive insights so that business teams can focus on high-value activities, not analysis.

Maxa's ERP machine intelligence solves three key business prediction challenges:

  • Reliable automated rolling forecasts for multi-variable business data;
  • Automated identification and prioritization of abnormal variations and deviations across key business activities;
  • Capturing the deep relationships and signals that traditional analytics cannot see.

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