Machine Learning Expertise:Understanding machine learning algorithms, neural networks, and deep learning frameworks (e.g., TensorFlow, Keras, PyTorch) is essential.
- Knowledge of data preprocessing, feature engineering, and model evaluation is valuable
- MLops - serving and life cycle of model
Data Architecture: Knowledge and ability to implement models, policies, rules and standards that govern how data is stored, arranged and integrated for business intelligence and analytic
Technical Skills - Data Movement & Transformation: Knowledge of tools, techniques, and processes to move (including extract, transform and load) an organization's electronic data, including
- Ability to configure Azure Data Factory as an orchestration methodology for data movements
- Understanding of integration runtimes and its use in data integration
- Ability to leverage Databricks notebooks for transformations
- Expertise in Azure Data Lake storage and security configuration
- Expertise in Azure Data Warehouse Synapse, Azure Database, and Azure Analysis Services technologies including development of optimized stored procedures, indexes, views
- Understanding of Logic Apps and Flows and its applicability in starting and stopping services
- Understanding of Kimball methodology for data warehouse lifecycle
- Experience with coding in SQL and scripting languages such as Python
- Analytical Thinking: Knowledge of techniques and tools that promote effective analysis and the ability to determine the root cause of organizational problems and create alternative solutions that resolve the problems in the best interest of the business.
- Learning Agility: Rapidly acquires new knowledge and learns new skills; can work productively in uncertain environments where roles and work are not clearly defined; finds opportunities in ambiguity; can identify opportunities for change; embraces and adapts to change; leans on credible sources from outside own group
