⚠️14+ Years of experience.
Below are the evaluation guidelines for the role:
1. Data Engineering
- Hands-on experience creating and managing ETL/data pipelines
- Experience designing Data Warehouses and Data Marts
- Large-scale data processing experience
2. Technical Skills
- Python scripting
- Shell scripting
- SQL – SQL-based technical evaluation
- Hadoop – Hive on Spark
- AWS / cloud-based data engineering experience
3. AI / LLM
- Evaluate basic LLM/AI knowledge and understanding
- Advanced Agentic AI or complex LLM questions are not required
Must-have:
- Strong Data Engineering / Big Data experience – building large-scale, fault-tolerant data platforms and pipelines.
- Python or Shell – advanced scripting proficiency.
- AWS – EC2, S3, EMR, Redshift, or equivalent cloud experience.
- Hadoop ecosystem – specifically Hive and Hive on Spark.
- ETL/ELT – hands-on pipeline development and database schema design.
- Strong SQL – analytical SQL, data marts, data warehousing, and analytic architecture.
- Agentic AI / LLM experience – practical experience using AI agents/LLM tools to automate parts of the Data Development Lifecycle.
- Large-scale production data – experience processing high data volumes.
- REST/JSON APIs – experience creating/consuming APIs and integrating systems.
- Production/Operational ownership – SLA, incident, and problem management.