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

Originally posted on Himalayas