Data Engineer

2070 Health is India s first venture studio dedicated to building patient-centric healthcare companies from scratch. By combining innovative ideas, exceptional founders, and a robust innovation platform, 2070 Health aims to transform the healthcare landscape in India. Through data-driven insights, we strive to improve patient outcomes and streamline healthcare delivery. Learn more at 2070health.com .

Job Summary:
As a Data Engineer at 2070 Health, you will design, build, and maintain robust data pipelines and infrastructure. You will work closely with cross-functional teams to enable data-driven decisions through efficient data storage, transformation, and visualization. The ideal candidate is proficient in Python, MySQL, AWS S3, Amazon Redshift, Excel, and Google Sheets, with experience in tools like dbt, Airbyte, and Apache Superset being a bonus.

Responsibilities:

Data Pipeline Development:

• Design, develop, and maintain scalable ETL (Extract, Transform, Load) processes.
• Build and maintain data transformation and automation workflows.
• Optimize data storage and retrieval processes using MySQL and Amazon Redshift.
• Ensure data accuracy, consistency, and reliability across various data sources.
  

Database Management:

• Manage and maintain MySQL and Redshift databases, including writing and optimizing SQL queries.
• Implement best practices for database performance, security, and scalability.
• Develop stored procedures, views, and functions to support business reporting needs.
  

Data Visualization and Reporting:

• Support data-driven decision-making by preparing reports and dashboards using Excel and Google Sheets.
• Create insightful visualizations using Apache Superset or other BI tools (bonus).
• Collaborate with stakeholders to understand reporting requirements and deliver timely insights.
  

Collaboration and Documentation:

• Work closely with Product Managers, Analysts, and other Engineers to understand data needs.
• Document data architecture, pipelines, and processes clearly and comprehensively.
• Support troubleshooting and resolution of data-related issues.

Continuous Improvement:

• Identify and implement improvements in data processes to enhance efficiency and reliability.
• Stay current with industry trends in data engineering and suggest relevant tools or technologies.