Job ID: QTAJH10601
Qloron Technology is looking for experienced Databricks Data Engineers who can join immediately and have solid hands-on expertise in modern data engineering technologies.
? Job Details• Job Title: Databricks Data Engineer
• Experience: 5–8 Years
• Joining: Immediate Joiners Preferred
? Key ResponsibilitiesData Pipeline Development
• Design, develop, and maintain scalable data pipelines using Python, PySpark, and SQL.
• Ingest, process, and transform data from multiple sources.
• Implement data quality checks and pipeline monitoring.
Databricks
• Use Databricks for data ingestion, ETL processing, and data engineering.
• Optimize Databricks jobs and clusters for performance and cost efficiency.
Data Warehouse & Modeling
• Build and optimize data warehouse solutions.
• Develop data models for analytical and reporting requirements.
• Apply strong understanding of ETL and data warehousing concepts.
Airflow
• Configure and manage Apache Airflow workflows.
• Develop and maintain DAGs for data pipeline orchestration and scheduling.
• Implement error handling and workflow monitoring.
Linux & Shell Scripting
• Work with Linux-based environments supporting data engineering workloads.
• Perform troubleshooting and server-level configuration activities.
• Use shell scripting for automation and operational tasks.
AI-Assisted Development
• Hands-on experience with AI coding assistants such as:
• Claude Code
• GitHub Copilot
• Codex
• Cursor
?️ Mandatory / Core Skills✅ Databricks
✅ PySpark
✅ Python
✅ SQL
✅ Airflow
✅ Data Modeling
✅ ETL / Data Warehousing
✅ Linux Administration
✅ Shell Scripting
✅ AI Coding Assistant experience
? Candidate Profile• 5–8 years of relevant Data Engineering experience.
• Strong problem-solving and analytical skills.
• Good understanding of scalable data pipelines and data processing.
• Strong communication and collaboration skills.
• Bachelor's degree in Computer Science, Engineering, or a related field preferred.
• Immediate
📌 Databricks Data Engineer (Hyderabad)
🏢 Qloron
📍 Hyderabad
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