Data Engineer

Remote
Full Time
Mid Level

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

 
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