ניצן כהן

Proficient in using Power BI to design and develop interactive dashboards and reports that deliver clear business insights.
Experienced in connecting to various data sources, modeling data with DAX, and building efficient data relationships to support self-service analytics.
Skilled in visual storytelling, KPI tracking, and creating intuitive, user-friendly interfaces for different stakeholders.
Used Power BI as part of end-to-end BI solutions, including data preparation with Power Query and publishing reports to the Power BI Service for ongoing collaboration and monitoring.

Extensive experience working with MSSQL for data extraction, transformation, and analysis in support of business and financial decision-making.
Skilled in writing complex SQL queries, stored procedures, views, and optimizing query performance.
Familiar with database schema design, data normalization, and managing relational data structures.
Used MSSQL as part of end-to-end data workflows, including integration with BI tools and automation of recurring data processes.

During my role as an Analyst, I worked extensively with advanced Excel tools for financial analysis, modeling, process automation, and the creation of complex reports.
Proficient in advanced functions (such as INDEX-MATCH, SUMIFS, IFERROR), Pivot Tables, conditional formatting, and building interactive dashboards.
Significant hands-on experience with Power Query for extracting data from various sources, cleaning, transforming, and enriching data as part of automated reporting and decision-support dashboards.

As part of my analytical roles, I worked with Business Intelligence tools and data platforms to generate actionable insights and support strategic decision-making.
Strong understanding of data pipelines, KPIs, and data visualization best practices.
Hands-on experience with querying and analyzing large datasets using SQL and BI tools (such as Power BI or Tableau), with a focus on translating business needs into clear, data-driven solutions.
Familiar with data science methodologies including data preprocessing, feature engineering, and statistical analysis. Exposure to working with large-scale data (Big Data) and understanding of data infrastructure in cloud-based environments.

Hands-on experience using Python for data analysis, automation, and process optimization.
Familiar with popular libraries such as pandas, NumPy, matplotlib, and scikit-learn for data manipulation, visualization, and basic machine learning tasks.
Used Python to streamline repetitive tasks, clean and transform data, and build tools that enhanced analytical workflows.
Comfortable working in Jupyter Notebooks and integrating Python scripts into broader data processes and reporting pipelines.