Azure Data and Analytics Engineer

Job Description

Job Purpose and Primary Objectives:

• Responsible for designing, developing, and implementing Data Processing Solutions using Azure Cloud architecture with structured and unstructured data sources
• Develop forward-thinking IT blueprints with technology roadmaps, and drive alignment and acceptance of the Enterprise architecture with multiple stakeholders.
• Ensure the security of data by maintaining the integrity and performance databases and ensure the data is stored securely and optimally connects and automates data sources, along with building visualizations.

Required Qualifications/Skills:

• Experience in Agile principles and Agile way of working
• Experience in Azure Cloud technologies (e.g. Azure Data Bricks, Azure Data Factory, Azure Function, Azure SQL DW, Key Vault, Storage Accounts, Azure DevOps, Azure Monitor). 
• Experience designing, developing, and implementing big data platforms using Azure Cloud architecture with structured and unstructured data sources.
• Experience in designing data solutions including Data Quality, Catalog, Lineage, Reconciliation, Error Handling.
• Experience in developing in languages e.g. Spark, python
• Knowledge of database models, SQL query, and data analysis
• Knowledge of integration with front end applications (Vue JS, API’s like Django)
• Strong problem solving and excellent communication skills, troubleshooting, and issue resolution
• Expertise in database development projects and ETL processes
• In-depth understanding of data management (e. g. permissions, security, and monitoring)
• Developing and maintaining data pipelines
• Comfortable with DevOps-driven teams

Azure Analytics Platform Engineer
Primary Responsibilities:

• Operationalizing machine learning models by adhering to the organizational compliance and processes
• Building robust data pipelines which will fulfill the model training/re-training and scoring
• Building platforms on Azure as Infrastructure as Code (IaC) which covers the Machine learning life cycle
• Complete workflow of the workload should be managed by adhering to the standards of CI/CD