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Hypersonix.ai is disrupting the e-commerce space with AI, ML and advanced decision capabilities to drive real-time business insights. Hypersonix.ai has been built ground up with new age technology to simplify the consumption of data for our customers in various industry verticals.

We are looking for a skilled and motivated Data Engineer to design, build, and maintain scalable data pipelines and data infrastructure. You will work closely with Data Science, Engineering, and Product teams to ensure high-quality, reliable, and accessible data for analytics, machine learning, and business applications.

Roles And Responsibilities

• Design, develop, and maintain scalable data pipelines for batch and real-time data processing
• Build and optimize ETL/ELT workflows to collect, transform, and load data from multiple sources
• Develop and maintain data models, data warehouses, and data lakes
• Ensure data quality, consistency, accuracy, and reliability across data pipelines
• Optimize data processing workflows for performance, scalability, and cost efficiency
• Work with large and complex datasets from multiple sources
• Collaborate with Data Scientists and ML Engineers to prepare and deliver high-quality datasets for machine learning models
• Develop data integrations with APIs, databases, cloud platforms, and third-party systems
• Monitor data pipelines and troubleshoot data processing and infrastructure issues
• Implement data validation, monitoring, and testing frameworks
• Maintain clear documentation of data pipelines, data models, and technical processes
• Follow best practices for data security, governance, and access control
• Contribute to the design and evolution of Hypersonix's data platform and architecture
  
  

• 5–7 years of experience in Data Engineering or a similar role
• Strong programming skills in Python or another programming language
• Strong experience with SQL and relational databases
• Hands-on experience building ETL/ELT pipelines and data workflows
• Experience with data processing technologies such as Spark/PySpark
• Experience working with cloud platforms such as AWS, Azure, or GCP
• Good understanding of data warehousing and data lake concepts
• Experience with workflow orchestration tools such as Airflow, Dagster, or similar
• Experience working with distributed systems and large-scale datasets
• Strong understanding of data modeling, database design, and performance optimization
• Familiarity with Git and CI/CD practices
• Good understanding of software engineering principles and coding best practices