Lead development of services and data infrastructure at the intersection of machine learning, big data and proprietary applications. Assembles large, complex data across all our data platforms and data tools. Identifies and defines the appropriate techniques for exposing data to Machine Learning systems. Creates and maintains critical data documentation and metadata that allows data to be understood and leveraged as a shared asset for data analytics. Assists in defining data modeling standards and foundational best practices. Provides inputs to the architectural design to make best use of the available resources, given goals, and expected loads for ML and analytics.

• Build data systems and pipelines


• Good in AWS services, primarily in BigData and ML stack (AWS EMR, Sagemaker to name a few)


• Prepare data for prescriptive and predictive modeling


• Build enterprise grade data lake to support both business analytical needs and next generation data infrastructure


• Design and implement best-in-class data architecture


• Building data integration toolkit for backend services


• Productionize data pipelines that supports building, training and deploying of machine learning models


• Work with unstructured or semi-structured data and load them into data warehouse (JSON, XML)


• Find better ways to move massive amounts of data from a variety of sources to formats consumable by reporting systems and people


• Collaborate with data scientists and application engineers