Your key responsibilities

Design, develop, and manage data solutions using Microsoft Fabric, including Lakehouse, Data Engineering, Pipelines, Spark, Notebooks, and KQL Database.
Implement ETL/ELT processes to ensure efficient data integration and transformation.
Create and maintain data models to support business intelligence and analytics initiatives.
Utilize Azure data storage services (e.g., Azure SQL Database, Azure Blob Storage, Azure Data Lake Storage) for effective data management.
Collaborate with cross-functional teams to gather requirements and deliver data solutions that meet business needs.
Write and optimize code in SQL, Python, PySpark, and Scala for data processing and analysis.
Implement DevOps practices and CI/CD pipelines for seamless data deployment and version control.
Monitor and troubleshoot data pipelines to ensure reliability and performance.
Communicate effectively with diverse teams to foster collaboration and knowledge sharing

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Libraries/Tools: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib

Data Analysis & Visualization: Power BI, Tableau, Excel

Database: MySQL, PostgreSQL, MongoDB

Machine Learning / AI experience

Big Data Tools (optional): Hadoop, Spark

Languages: Python, Java, JavaScript, C++, C#, etc.

Frameworks: React, Angular, Node.js, Django, Flask, .NET (based on your stack)

Version Control: Git, GitHub

Databases: SQL & NoSQL

DevOps tools (optional): Docker, Kubernetes, Jenkins

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• A solid understanding of retail industry dynamics, including key performance indicators (KPIs) such as sales trends, customer segmentation, inventory turnover, and promotions.
• Strong ability to communicate complex data insights to non-technical stakeholders, including senior management, marketing, and operational teams.
• Meticulous in ensuring data quality, accuracy, and consistency when handling large, complex datasets.
• Gather and clean data from various retail sources, such as sales transactions, customer interactions, inventory management, website traffic, and marketing campaigns.
• Strong proficiency in Python for data manipulation, statistical analysis, and machine learning (libraries like Pandas, NumPy, Scikit-learn).
• Expertise in supervised and unsupervised learning algorithms
• Use advanced analytics to optimize pricing strategies based on market demand, competitor pricing, and customer price sensitivity.

Good to have skills:

• Familiarity with big data processing platforms like Apache Spark, Hadoop, or cloud-based platforms such as AWS or Google Cloud for large-scale data processing.
• Experience with ETL (Extract, Transform, Load) processes and tools like Apache Airflow to automate data workflows.
• Familiarity with designing scalable and efficient data pipelines and architecture.
• Experience with tools like Tableau, Power BI, Matplotlib, and Seaborn to create meaningful visualizations that present data insights clearly

Professional & Technical Skills:

• Strong analytical and statistical skills.
• Expertise in machine learning and AI.
• Experience with retail-specific datasets and KPIs.
• Proficiency in data visualization and reporting tools.
• Ability to work with large datasets and complex data structures.
• Strong communication skills to interact with both technical and non-technical stakeholders.
• A solid understanding of the retail business and consumer behavior.
• Programming Languages: Python, R, SQL, Scala
• Data Analysis Tools: Pandas, NumPy, Scikit-learn, TensorFlow, Keras
• Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn
• Big Data Technologies: Hadoop, Spark, AWS, Google Cloud
• Databases: SQL, NoSQL (MongoDB, Cassandra)

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