Database Engineer
bebo Technologies
- Location
- Chandigarh, India
- Job type
- Full-time
Required skills
- Python
- AWS
- Azure
- CSV
- data ingestion
- data lake
- data modeling
- Databricks
- ETL
- IoT
- JSON
- Kafka
- Lambda
- microservices
- Parquet
- Serverless
- Snowflake
- SQL
- REST APIs
About the role
bebo Technologies
Website:
bebotechnologies.com
Job details:
Key Responsibilities
- Develop and maintain scalable data pipelines for batch and real-time data processing
- Build robust data ingestion frameworks using APIs, file-based ingestion, and streaming sources
- Design and implement Python and PySpark-based data transformation logic for large-scale datasets
- Develop Python-based microservices and REST APIs for secure and efficient data access and sharing
- Integrate data pipelines with platforms such as Databricks, Delta Lake, and Snowflake
- Experience with serverless data processing (e.g., AWS Lambda, Azure Functions with Python
- Implement and manage streaming pipelines using Kafka / Kinesis
- Work with structured, semi-structured, and unstructured data (Parquet, JSON, CSV, sensor/IoT data
- Optimize data processing jobs for performance, scalability, and cost efficiency
- Apply best practices for data partitioning, storage optimization, and query performance tuning
- Ensure data quality, validation, and monitoring across pipelines
- Collaborate with data architects, analysts, and downstream consumers to deliver reliable datasets
- Support data modeling and transformation aligned with Lakehouse principles (e.g., medallion architecture)
- Contribute to automation and deployment of pipelines using orchestration and CI/CD practices
Required Skills & Experience
- 2–4 years of hands-on experience in Data Engineering
- Strong programming expertise in Python and PySpark
- Solid experience in SQL for data transformation and analysis
- Hands-on experience building data pipelines (ETL/ELT) in production environments
- Experience working with APIs and microservices for data ingestion and exposure
- Strong understanding of data processing frameworks such as Spark, AWS Glue, or Dbt
- Experience handling large-scale datasets across multiple formats (Parquet, JSON, CSV, etc)
- Good understanding of Data Lake / Lakehouse concepts and implementation patterns
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