Associate Principal - Machine Learning/Big Data Analytics at Rinalytics Advisors
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Head - Data Engineering - IT (12-16 yrs)
An accomplished data engineering leader with proven experience in architecture and building large scale distributed applications and platform to acquire, process, analyse data in real-time as well batch.
- An ideal candidate is expected to lead 3 member data engineering team and would bring strong data architecture and data engineering expertise.
- Creation & execution of the vision behind the core Data Engineering group through development and deployment of data infrastructure roadmap for the business.
- Play a critical role in the development of the company's Technology roadmap, infrastructure and platform; lead the implementation of DWH and query building technology that aligns with business strategy.
- Provide high-level leadership to the Data Infrastructure/platform development and improve the functionality, reliability, scalability by bringing industry best-practice and technology trends into the continued innovation to Data platform.
- Own and extend the data pipeline through the data collection, data storage, data processing, and data transformation of large data-sets by using DWH, ETL, Redshift and Sql.
Essential Duties &Responsibilities
- Minimum Bachelor's degree in Computer Science, Engineering or Technology.
- 12 -16 years of experience in Data architecture, data engineering, and/or preferably in product development, designing and implementing data architectures, data pipelines for large data sets.
- Strong expertise in BI architectures, Redshift, sql, data warehousing, data modeling, and Big Data.
- Design, develop and deploy state-of-the-art data models, DWH and data pipeline/ETL using emerging technologies and tools
- Define standards, methodologies for Data Warehousing environment and design & build highly scalable data pipelines using new generation tools and technologies like Redshift, DWH and big data to induct data from various platforms.
- Translate critical consumer business insights into scalable technical architectures meeting data warehousing design standards.