Mid. AI Engineer (Data Warehouse)

Job Description

  1. Design and develop data warehouse systems including data structure planning and integration across multiple sources to support AI and analytics.
  2. Build and manage efficient, scalable data pipelines for extraction, transformation, validation, and integration using tools like Apache Airflow, dbt, or NiFi.
  3. Perform data cleaning, transformation, and validation to ensure high-quality datasets for AI training and analytics.
  4. Prepare and manage AI training datasets, validating data consistency and accuracy.
  5. Develop dashboards and reports using visualization tools such as Power BI, Metabase, or Superset.
  6. Maintain technical documentation and version control for data pipelines and datasets to ensure traceability and consistency.
  7. Collaborate with AI Engineers, Data Scientist and stakeholders to fulfill data needs and optimize system performance.
  8. Take ownership of data quality standards and prioritize tasks for pipeline and visualization development.
  9. Propose improvements and upgrades for tools and technologies in data management.
  10. Identify and manage technical and operational risks related to data and pipelines.


Job Requirements

  1. Graduate with a Bachelor, Diploma 3, or Diploma 4 degree in IT, Computer Science, or related fields.
  2. Have a minimum 2 years of experience as an AI Engineer with a strong focus on data warehouse system and pipeline development. 
  3. Good critical thinking and problem-solving skills with ability to identify and mitigate risks in data pipelines.
  4. Strong communication skills to collaborate effectively with AI Engineers, Data Scientists, and business stakeholders.
  5. Skilled in designing data warehouse systems, including data structures, storage schema, and integrating data from multiple sources for AI and analytics.
  6. Experienced with designing and maintaining ETL pipelines using tools like Apache Airflow, dbt, or NiFi.
  7. Proficient in data cleaning, transformation, validation, and preparing data for AI model training and analytics.
  8. Able to visualize data using Power BI, Metabase, or Superset.
  9. Capable of writing and maintaining technical documentation and managing dataset versioning.
  10. Understands data governance, corporate, management, and basic security principles relevant to AI development.

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