bebo Technologies is a leading complete software solution provider. bebo stands for 'be extension be offshore'. We are a business partner of QASource, inc. USA[www.QASource.com]. We offer outstanding services in the areas of software development, sustenance engineering, quality assurance and product support. bebo is dedicated to provide high-caliber offshore software services and solutions.
Our goal is to 'Deliver in time-every time'.
For more details visit our website: www.bebotechnologies.com
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https://www.youtube.com/watch?v=S1Bgm07dPmMKey
Key Responsibilities
Design and implement scalable data platforms leveraging Data Lake, Lakehouse, and Data Mesh architectures
Build and optimize data pipelines for batch and real-time processing using tools like Databricks, Spark, DBT, and cloud-native services·
Develop robust data ingestion frameworks for structured, semi-structured, and unstructured data (APIs, files, streaming sources)
Design and develop scalable Python-based micro services to enable secure and efficient data sharing across systems and applications
Build RESTful APIs and/or event-driven services for exposing curated datasets from Data Lake/Lakehouse platforms
Work extensively with Python, PySpark, and SQL for data transformation and processing
Implement streaming pipelines using Kafka / Kinesis and integrate with downstream analytics systems
Design and manage large-scale datasets using formats such as Parquet, JSON, CSV, and sensor/IoT data
Optimize data storage, partitioning, and query performance for high-volume analytical workloads· Collaborate with cross-functional teams (Data Architects, Analysts, BI teams) to operationalize data lake solutions
Contribute to data modeling in Lakehouse environments (medallion architecture, dimensional modeling)
Ensure data quality, reliability, and observability across pipelines
Implement serverless data processing solutions where applicable
Required Skills & Experience
4–6 years of experience in Data Engineering or related roles
Strong expertise in Python, PySpark, and SQL
Hands-on experience with Databricks, Delta Lake, or Snowflake
Experience with ETL / ELT frameworks and orchestration tools
Practical exposure to streaming technologies like Kafka or Kinesis
Strong understanding of batch processing frameworks such as Spark, AWS Glue, or DBT
Proficiency in handling structured, semi-structured, and unstructured data
Experience with modern data modeling techniques (Star Schema, Snowflake Schema, 3NF)
Good understanding of Data Lakehouse concepts and implementation patterns
Familiarity with serverless architectures (e.g., Python-based serverless pipelines)