Description

• Design and Build: Develop and implement scalable, secure, and cost-effective cloud-based

data solutions.

• Manage Data Pipelines: Develop and maintain data pipelines to extract, transform, and load

data into data warehouses or data lakes.

• Ensure Data Quality: Implement data quality and validation processes to ensure data

accuracy and integrity.

• Optimize Data Storage: Ensure efficient data storage and retrieval for optimal performance.

• Collaborate and Innovate: Work closely with data architects, data modelers, data product

managers, and stay updated with the latest cloud technologies and best practices.

• Mentoring: Guide & groom associate data engineers & data engineers (external/internal) to

ensure timely deliverables fulfilling all data engineering guidelines & standards.

Technical Requirements:

• Programming: Python, PySpark, SQL

• ETL & Integration: Databricks (Mandatory), AecorSoft/DataSphere,

• Data Warehousing: SCD Types, Fact & Dimensions, Star Schema, ETL

• Data Modeling: Erwin (Optional)

• Medallion Architecture: Bronze, Silver, Gold, Platinum

• AWS Cloud Services: S3, Glue, Redshift, Lambda, EMR, Athena

• GenAI exposure: Gemini/Genie/Kiro etc.

• Supporting Technologies: Databricks Jobs, Airflow

• Visualization (Optional): PowerBI (Optional), Tableau (Optional), GenBI (Optional)

Soft Skills:

• Problem-Solving: The ability to identify and solve complex data-related challenges.

• Communication: Effective communication skills to collaborate with Engineering Leads, Data

Product Managers, Data Modeler, Data Analysts, and stakeholders.

• Analytical Thinking: The capacity to analyze data and draw meaningful insights.

• Attention to Detail: Meticulousness in data preparation and pipeline development.

• Adaptability: The ability to stay updated with emerging technologies and trends in the data

engineering field

Key Skills

Education

Bachelor's degree