Business Intelligence VS Data Analysis VS Data Engineering

Business Intelligence (BI) Developers focus on transforming raw data intoactionable insights through the development of interactive dashboards, reports, and analytics tools.

Data Analysts are experts in analyzing and interpreting data to derive meaningful insights. They work closely with stakeholders to understand business requirements and provide data-driven recommendations.

Data Engineers are the architects of data infrastructure. They are responsible for designing, building, and maintaining the systems and pipelines that enable the smooth flow of data.

• Designing and developing interactive dashboards and reports using BI tools (e.g., Power BI, Tableau).
• Extracting data from various sources and transforming it into a format suitable for analysis.
• Creating data visualizations to communicate complex information in a clear and intuitive manner.
• Collaborating with stakeholders to understand reporting requirements and designing effective solutions.
• Ensuring data accuracy, performance, and security in BI solutions.

• Gathering and cleaning data from various sources for analysis.
• Performing exploratory data analysis to identify patterns, trends, and correlations.
• Applying statistical techniques and data modeling to extract insights and make predictions.
• Creating data visualizations and reports to communicate findings to stakeholders.
• Collaborating with business teams to identify data-driven opportunities and solutions.

• Designing and developing data pipelines for data extraction, transformation, and loading (ETL) processes.
• Building and managing databases and data warehouses to ensure efficient storage and retrieval of data.
• Implementing data integration and data quality processes to ensure data accuracy and reliability.
• Collaborating with cross-functional teams to understand data requirements and designing scalable solutions.
• Optimizing data infrastructure for performance, scalability, and cost efficiency.

• Proficient in SQL for data querying and manipulation
• Experience with data modeling and designing database schemas
• Strong knowledge of data visualization tools like Power BI, Tableau, or Looker
• Understanding of business intelligence concepts and best practices
• ETL (Extract, Transform, Load) and data integration skills
• Familiarity with data warehouse design and implementation
• Ability to create interactive dashboards and reports
• Basic programming skills in languages like Python or R
• Knowledge of dimensional modeling and OLAP (Online Analytical Processing)
• Strong problem-solving and troubleshooting abilities

• Proficiency in SQL for data extraction and analysis
• Data visualization skills using tools like Tableau, Power BI, or QlikView
• Strong analytical and problem-solving abilities
• Familiarity with statistical analysis and data exploration techniques
• Knowledge of data cleaning and data quality assurance
• Experience in data interpretation and drawing meaningful insights
• Excellent communication and storytelling skills to convey findings
• Basic understanding of programming languages like Python or R
• Domain knowledge in the relevant industry or business context

• Proficient in programming languages such as Python, Java, or Scala
• Strong knowledge of database systems and SQL
• Experience with big data processing frameworks like Hadoop, Spark, or Kafka
• ETL (Extract, Transform, Load) and data integration skills
• Familiarity with cloud platforms like AWS, Azure, or GCP
• Understanding of data modeling and database design principles
• Knowledge of data warehousing concepts
• Ability to work with structured and unstructured data
• Experience with data pipeline and workflow management tools like Apache Airflow or Luigi

MSBI (SSRS, SSIS, SSAS), Power BI, Tableau, QlikView, SQL, ETL/ELT, Data modeling, Data warehousing, Dimensional modeling, OLAP, Dashboards, Reports, Data visualization

SQL, Python, R, Tableau, Power BI, Excel, Google Analytics, Google Data Studio, Apache Zeppelin, Jupyter Notebooks, Data visualization

Apache Spark, Hadoop, SQL, Python, Talend, Informatica, Apache Kafka, Apache Airflow, AWS Glue, Azure Data Factory, Google Cloud Dataflow, ETL/ELT