Data Engineer

Location: Remote

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.
 

• 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 Hypersonixs 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