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