- 6+ years of experience in data engineering, manipulation, and analytics support roles across various domains

- Strong hands-on experience in building ETL pipelines, data cleansing, and normalization using R, Python and SQL

- Proficient in integrating data across cloud and on-premise systems, managing data integrity, and performing advanced transformations

- Supported business teams with structured datasets for reporting, dashboards, and ad hoc analysis

Key Responsibilities :

- Data Storage and Organization : Designing, implementing, and maintaining databases and data warehouses to store and organize data effectively.

- Data Integrity and Quality : Ensuring data accuracy, consistency, and reliability through validation, cleansing, and error handling.

- Data Security and Access Control : Implementing security measures to protect sensitive data and managing access permissions for authorized users.

- Data Manipulation and Transformation : Using programming languages like Python, R, or SQL to transform and manipulate data for analysis and reporting.

- Data Analysis and Reporting : Assisting in data analysis, creating reports, and providing insights to support business decision-making.

- Metadata Management : Managing metadata (data about data) to improve data discoverability and usability.

- Data Lifecycle Management : Overseeing the entire lifecycle of data, from creation to archival or deletion.

Skills Required :
- Data Management : Strong understanding of data management principles, including data modeling, data warehousing, and data governance.

- Data Manipulation : Proficiency in programming languages like Python, R, or SQL for data manipulation and analysis.

- Database Management : Experience with database technologies (e.g., SQL Server, Oracle, MySQL).

- Analytical Skills : Ability to analyze data, identify trends, and derive meaningful insights.

- Problem-Solving : Ability to troubleshoot data-related issues and implement effective solutions.

- Communication Skills : Effectively communicate data findings to technical and non-technical audiences.