Data Engineer

• Data availability and Architecture Acceleration: Responsible for developing data integration and implementation solutions that ensure data availability.

• Project Delivery: Involved in on-time delivery of projects.

• Software Development Lifecycle Participation: Engages in the full software lifecycle, including development, testing, and deployment of data-related applications, which may involve databases in on-premises or cloud environments.

• Innovation and Transformation: Has opportunities to innovate and transform data infrastructure and processes to improve efficiency and performance.

• Cross-Functional Collaboration: Works closely with various teams such as engineering and platform teams to align operational goals with technical execution.

Minimum Qualifications 

• Bachelor’s degree in engineering, Technology, or a related field. 

• Collage pass out having good understanding of Data Warehousing (DWH), Data transformation and pipeline. 

• Good understanding in cloud data platforms and database technologies: Preferably in Snowflake 

• Good understanding with ETL/ELT tools: Informatica, DBT (Preferably). 

• Good understanding with data streaming technologies: Kafka 

• Hands-on scripting and programming skills: Unix shell Script, Python, SQL

• Good understanding of Enterprise AI integrations : MCP Server, RAG

Preferred Qualifications 

• Understanding the real-time data and stream processing systems. 

• Familiarity with data visualization tools and techniques. 

• Knowledge of data governance, security, and compliance practices is an added advantage. 

• Exposure to AI/ML frameworks and technologies in the data domain (LLMs, predictive/prescriptive analytics, etc.) is an added advantage. 

• Good team player with strong communication skills.