Experience:
Demonstrated experience in successfully delivering multiple end-to-end data analytics projects on Azure, following the complete software development life cycle (SDLC). Proven track record of building data pipelines for streaming, near-real time, and batch data processing, utilizing the various technologies available in Azure. Hands-on experience in developing comprehensive end-to-end solutions, with the ability to understand the big picture and contribute to and guide the team in delivering projects within specified timeframes and budgets.
Data Analysis. Able to participate in business discussions and assist an Architect with gathering data requirements. Good analytical and problem-solving skills to help address data challenges.
Advanced SQL Skills. Proficiency in writing complex SQL queries for data extraction, transformation, and analysis. Knowledge of SQL functions, joins, subqueries, and performance tuning. Able to navigate source systems with minimal guidance to understand how data is related and use like data profiling to gain a better understanding of the data.
Azure Data Factory. Experience in creating and managing data pipelines using Azure Data Factory. Understanding of data integration, transformation, and workflow orchestration in Azure environments.
Azure Databricks. Knowledge of data engineering workflows and best practices in Databricks. Able to code Databricks Notebook and understand existing templates and patterns for development.
Data Models. Understanding of Dimensional and relational data models and experience in write efficient code (in SQL and PySpark) to populate data models.
Python (Desired). Programming skills in Python, particularly for data manipulation and analysis.
Automation: Experience developing solutions to automate data validations tasks using SQL/Python scripts.
Version Control and Collaboration. Proficiency in using Git for version control and collaboration in data projects. Ability to work effectively in a team environment, especially in agile or collaborative settings.
Communication and Documentation. Clear and effective communication skills to articulate findings and recommendations for other team members. Ability to document processes, workflows, and data analysis results effectively.
Continuous Learning and Adaptability. Willingness to learn new tools, technologies, and techniques as the field of data analytics evolves. Being adaptable to changing project requirements and priorities.
