Data Engineer

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