Overview

Key Responsibilities :

Responsibilities

• Understand the factories , manufacturing process , data availability and avenues for improvement
• Brainstorm , together with engineering, manufacturing and quality problems that can be solved using the acquired data in the data lake platform.
• Define what data is required to create a solution and work with connectivity engineers , users to collect the data
• Create and maintain optimal data pipeline architecture.
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery for greater scalability
• Work on data preparation, data deep dive , help engineering, process and quality to understand the process/ machine behavior more closely using available data
• Deploy and monitor the solution
• Work with data and analytics experts to strive for greater functionality in our data systems.
• Work together with Data Architects and data modeling teams.
• Good knowledge of the business vertical with prior experience in solving different use cases in the manufacturing or similar industry
• Ability to bring cross industry learning to benefit the use cases aimed at improving manufacturing process

Qualifications

• Strong analytic skills related to working with unstructured datasets.
• Build processes supporting data transformation, data structures, metadata, dependency and workload management.
• Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores
• Ability to foresee and identify all right data required to solve the problem
• Strong skill in data mining, data wrangling techniques for creating the required analytical dataset
• Adaptive mindset to improvise on the data challenges and employ techniques to drive desired outcomes
• Know how of any visualization tools such as PowerBI, Tableau
• Good storytelling skills to present the data in straightforward and meaningful manner
• Strong skill in data analysis techniques to generate finding and insights by means of exploratory data analysis
• Positive understanding of how to transform and connect the data of various types and form
• Great numerical and analytical skills
• Identify opportunities for data acquisition
• Explore ways to enhance data quality and reliability
• Build algorithms and prototypes
• Reformulating existing frameworks to optimize their functioning.
• Good understanding of optimization techniques to make the system performant for requirements. .