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