BI Engineer / Data Scientist

Experience: 6+ years of experience working as a BI engineer or Data Scientist
Good communication skills
Excellent understanding of data warehousing concepts, including dimensional modeling, data normalization, and star schema design.
Proficiency in SQL for querying and manipulating data in RDBMS such as MySQL, PostgreSQL, SQL Server, or Oracle.
Experience with ETL tools like Informatica, Talend, SSIS, or Apache NiFi
Good knowledge of data visualization tools such as Tableau, Power BI, Looker, or QlikView.
Proficiency in programming languages like Python or R
Knowledge of statistical analysis techniques and experience with statistical modeling tools like R, Python libraries (eg, pandas, numpy, scipy), or SAS for advanced analytics and predictive modeling.
Familiarity with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) and experience with cloud-based data services like Amazon Redshift, Azure SQL Data Warehouse, or Google BigQuery.
Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
Experience with version control systems like Git
Ability to write scripts for automation tasks using languages like Python, Shell scripting, or PowerShell to streamline ETL processes and other repetitive tasks.
Understanding of data security principles and experience with implementing security measures to protect sensitive data, including encryption, access control, and compliance with data privacy regulations (eg, GDPR, CCPA).
Any special requirements such as weekend work
While not necessary, we could benefit if you have previously worked in the domain of Leadership Development / Learning and Development / EdTech.

Data Modeling and Design: Designing and developing data models and databases to support business reporting and analytics needs.
Data Extraction and Transformation: Extracting data from various sources such as databases, data warehouses, and APIs, and transforming it into a format suitable for analysis.
Data Warehousing: Building and maintaining data warehouses or data marts to store structured and unstructured data efficiently.
ETL Processes: Developing and managing Extract, Transform, Load (ETL) processes to move data from source systems to the data warehouse, ensuring data integrity and quality.
Data Visualization and Reporting: Creating dashboards, reports, and visualizations using Power BI to present data insights to stakeholders in an understandable and actionable format.
Data Analysis: Conducting exploratory data analysis to identify trends, patterns, and insights that can inform business decisions.
Performance Tuning and Optimization: Optimizing data queries and processes to improve performance and ensure scalability of BI solutions.
Collaboration with Stakeholders: Collaborating with business stakeholders to understand their requirements and translate them into technical solutions.
Data Governance and Security: Implementing data governance policies and ensuring compliance with data privacy regulations to protect sensitive information.
Continuous Improvement: Keeping abreast of emerging technologies and best practices in BI and analytics, and continuously improving processes and solutions to meet evolving business needs.
Documentation and Knowledge Sharing: Documenting data models, processes, and solutions, and sharing knowledge with team members to ensure continuity and scalability of BI initiatives.
Project Management: Managing BI projects from conception to completion, including requirements gathering, planning, execution, and monitoring.