Hypersonix Inc
Website:
hypersonix.ai
Job details:
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
Requirements
- 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
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