bebo Technologies
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Chandigarh · On-site · 6-9 Yrs · Sep 9
Software Development · 1001 to 5000 Employees · Chandigarh, India
bebo- Be Extension, Be Offshore bebo Technologies has a dedicated, experienced, and skilled group of professional engineers that utilize cutting-edge methodologies, tools, and techniques to develop state-of-the-art solutions for clients. Founded in August 2002, bebo Technologies has completed hundreds of projects for clients such as Amazon, Adobe, Ford, Apple, Cisco, IBM, and Oracle. You will work with teams of experienced and skilled professionals who consistently research, evaluate, and develop cutting-edge solutions by utilizing the latest tools and technologies. At bebo, we consistently work with our team members to ensure they are set up for success and share bebo Technologies' dedication to excellence. We optimize our client's software development lifecycle, ensuring we meet all of their QE and testing objectives and schedules. Our engineers have years of knowledge and experience in web, SaaS, client-server, desktop, mobile, and back-office domains. At bebo Technologies, you will work with modern programming languages and database structures to ensure that the client's application is ready to run on the servers and operating systems based on their requirements.
Hiring: Engineering
What employees say
+ Great environment and opportunity of growth
− For now, nothing to add here
More reviews on the company profile.
Interview experience · Software Engineer (Golang)
interview process of good, no delay in interview scheduling. The the interviews were easy and more like general interview. they were asking question were not related to my job title. I also felt like the organization str
Easy difficulty · Chandigarh · 54 reports
More interview insightsJob description
Overview
Key Responsibilities
Responsibilities
- Design and implement scalable data platforms leveraging Data Lake, Lakehouse, Data Mesh, and modern data architecture patterns.
- Build and optimize data pipelines for batch and real-time processing using Databricks, Apache Spark, dbt, and cloud-native AWS services.
- Develop robust data ingestion frameworks for structured, semi-structured, and unstructured data from APIs, files, databases, and streaming sources.
- Design and develop scalable Python-based microservices to enable secure and efficient data sharing across systems and applications.
- Build RESTful APIs and event-driven services for exposing curated datasets from Data Lake and Lakehouse platforms.
- Work extensively with Python, PySpark, and SQL for data transformation, processing, and data engineering workflows.
- Implement streaming data pipelines using Kafka/Kinesis and integrate them with downstream analytics and data platforms.
- Design and manage large-scale datasets using formats such as Parquet, JSON, CSV, and IoT/sensor data.
- Optimize data storage, partitioning, and query performance for high-volume analytical workloads.
- Design and implement data orchestration workflows using Apache Airflow and/or Databricks Workflows.
- Implement infrastructure and data platform components using Terraform and Infrastructure as Code (IaC) practices.
- Containerize data applications and services using Docker where applicable.
- Collaborate with cross-functional teams, including Data Architects, Data Analysts, and BI teams, to operationalize Data Lake and Lakehouse solutions.
- Contribute to data modeling in Lakehouse environments, including Medallion Architecture and dimensional modeling.
- Implement data quality, validation, monitoring, and observability practices across data pipelines.
- Use data quality and observability tools to identify data issues, monitor pipeline health, and improve data reliability.
- Ensure data quality, reliability, security, and observability across data pipelines.
- Implement serverless data processing and pipeline solutions where applicable.
Qualifications
- 6-8 years of experience in Data Engineering or related roles.
- Strong understanding of modern data architectures such as Data Lake, Lakehouse, Data Mesh, and Data Products.
- Strong proficiency in Python, PySpark, and SQL.
- Hands-on experience with Databricks, Delta Lake, and/or Snowflake.
- Hands-on experience with Apache Airflow and/or Databricks Workflows.
- Practical experience with streaming technologies such as Kafka and/or AWS Kinesis.
- Strong understanding of batch processing frameworks and technologies such as Apache Spark, AWS Glue, and dbt.
- Proficiency in handling structured, semi-structured, and unstructured data.
- Familiarity with vector databases and data architectures supporting AI/ML use cases.
- Hands-on experience with AWS services including S3, Glue, Glue Data Catalog, Athena, and Redshift.
- Good understanding of cloud security concepts, including IAM, encryption, access control, and data governance.
- Strong understanding of Data Lakehouse concepts, architecture, and implementation patterns.
- Familiarity with serverless architectures and Python-based serverless data processing solutions.
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Last updated 18 September 2026
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