Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.
This is a hands-on engineering role focused on designing efficient data pipelines, improving data infrastructure, and enabling teams to leverage data effectively.
Responsibilities- Design, build, and maintain scalable data pipelines and ETL/ELT workflows to ingest and transform data from multiple sources
- Develop and optimize batch and near real-time data processing pipelines for analytics and reporting
- Build and maintain data warehouse and data lake structures to support business intelligence and analytics use cases
- Implement and maintain data models that support efficient querying and reporting
- Improve performance and scalability of data systems through query optimization, indexing, and partitioning strategies
- Implement data quality checks, monitoring, and logging to ensure reliability of data pipelines
- Exposure to AI initiatives and experience building data pipelines supporting AI workflows
- Work with data architects and engineering teams to implement scalable data platform designs
- Collaborate with analysts, BI developers, and business stakeholders to deliver data solutions that support business needs
- Maintain documentation for data pipelines, data models, and data workflows
Bachelor's/Master's in Engineering 5-8 years
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