AI Data Engineer

Job Description:

Responsibilities:

Collaborate with data modelers to prepare and optimize datasets for AI model training and inference.
Design and implement data pipelines that support AI/ML workflows, including feature engineering and model monitoring.
Integrate AI-powered analytics and predictive models into business intelligence tools like Power BI.
Evaluate and implement AI services (e.g., Azure Cognitive Services, OpenAI, or custom ML models) to enhance data products and user experiences.

Required Skills and Experience:

SQL, DBT, ADF, DAX, Power BI, Snowflake
AI/ML Integration: Experience integrating AI/ML models into data pipelines and analytics platforms.
Data Modeling: Hands-on experience in designing and implementing complex data models, with a strong understanding of normalization, denormalization, and schema designs such as star schema and snowflake schema.
Ingestion Processes: Hands-on experience in developing and optimizing EL (Extract and Load) processes using ADF (Azure Data Factory).
Data Transformation Processes: Hands-on experience in developing and optimizing DBT (Data Build Tool) models, including data testing.
Data Warehousing: Understanding of data warehousing concepts and best practices, particularly with the Snowflake platform, including optimization strategies, query tuning, and clustering.
Cloud Platforms: Experience with Azure, particularly in relation to data storage, integration, processing, and AI services.
Programming Languages: Proficiency in SQL and DAX; familiarity with Python or R for AI/ML tasks is a plus.
Visualization Expertise: Experience creating interactive and performant visualizations using Power BI, including designing and maintaining semantic models.
AI Training & Development: Experience working with the data, systems, and architecture to train and develop new AI-powered analytics and functionality.

Relevant Certifications:

Skill:

Any Graduate

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