Candidates
must have hands-on experience
in all of the following technologies:
✅
Apache Airflow
✅ Data Lake / Data Lake Architecture
✅ SQL
✅ Apache Iceberg
✅ dbt (Data Build Tool)
✅ PySpark
⚠️ All listed skills are mandatory. Candidates without hands-on experience in any of these technologies may not be considered.
? Key Responsibilities
- Design, develop and maintain scalable data engineering pipelines.
- Build and orchestrate data workflows using
Apache Airflow
.
- Develop and optimize data processing solutions using
PySpark
.
- Work with contemporary
Data Lake architectures
and large-scale datasets.
- Implement and manage
Apache Iceberg
tables and data pipelines.
- Develop reliable transformation models using
dbt
.
- Write complex and optimized
SQL queries
for data extraction, transformation and analysis.
- Build batch and incremental data processing pipelines.
- Implement data quality,
validation and monitoring frameworks.
- Optimize data pipelines for performance, scalability and cost.
- Collaborate with Data Architects, Analytics Engineers and other technical teams.
- Troubleshoot pipeline failures and production data issues.
- Follow best practices for data governance, security and CI/CD.
? Required Technical Expertise
Data Engineering
- Strong Data Engineering fundamentals
- Data Lake / Lakehouse architecture
- Batch and incremental processing
- ETL/ELT pipeline development
Pipeline Orchestration
- Apache Airflow
- DAG development
- Scheduling and dependency management
- Pipeline monitoring and troubleshooting
Big Data
- PySpark
- Distributed data processing
- Performance optimization
Data Lake / Lakehouse
- Apache Iceberg
- Partitioning
- Schema evolution
- Time travel
- Snapshot management
Transformation
- dbt
- Data modeling
- Incremental models
- Testing and documentation
Database
- Advanced SQL
- Qu
📌 Data Engineer – Airflow | Data Lake | SQL | Iceberg | dbt | PySpark (Bengaluru)
🏢 Qloron
📍 Bengaluru
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