Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, batch and near-real-time processing, data warehouse and data lake structures, data models, query optimization, and data quality monitoring. Support AI workflows, collaborate with architects, analysts, and stakeholders, and maintain documentation for data platforms and pipelines.
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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