• Citizen

• Full Time

• Direct Hire

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 8th Sep 2022

Essential Functions & Key Responsibilities

Work with data scientists to operationalize machine learning models. Build Machine learning deployment pipelines,

including feature generation and extraction, and data wrangling.

• Design and develop scalable solutions to store and retrieve high-volume timeseries data for various use cases including fast data

access and data science experimentation.

Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources

using AWS 'big data' technologies.

Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency,

and other key business performance metrics.

• Build and maintain data quality validation and testing software.

Troubleshoot and optimize production issues.

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, such as automating manual processes, optimizing data

delivery and redesigning infrastructure for greater scalability.

Create data tools for analytics and data scientist team members that assist them in building and optimizing our products into an

innovative industry leader.

• Work with data and analytics experts to strive for greater functionality in our data systems.

Education, Qualifications and Skills required:

• Bachelor's degree in computer programming, computer science, or a related field.

Minimum of 5 years' work experience, preferably in product development.

Experience building and optimizing 'big data' data pipelines, architectures, and data sets.

Experience performing root cause analysis on internal and external data and processes to answer specific business questions and

identify opportunities for improvement.

Strong analytical skills related to working with unstructured datasets.

Experience building and optimizing 'big data' data pipelines, architectures, and data sets.

• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and

identify opportunities for improvement.

Strong analytical skills related to working with unstructured datasets.

• Experience building processes supporting data transformation, data structures, metadata, dependency, and workload

management.

Successful track record of manipulating, processing, and extracting value from large, disconnected datasets.

• Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
• Strong organizational & communication skills.

Technology Skills:

• Build scalable data pipelines to ingest & transform streaming & batch data using distributed technologies like Apache Spark,

Kafka, AWS Kinesis.

Advanced working SQL knowledge and experience working with object Oriented databases, query authoring (SQL) as well as

working familiarity with a variety of databases

Relational SQL (Postgres preferred) and NoSQL databases (Mongo preferred)

AWS cloud services: EC2, Lambda, EMR, RDS, Redshift

Stream-processing systems (Spark-Streaming preferred)

Object-oriented/object function scripting languages: Python, Java, Scala, etc.

Experience working with a Data Lake

Databricks, Kubernetes experience/ exposure

Big Data ML toolkits experience/ exposure

Data warehousing or business intelligence schema design experience

Experience with IOT/ Timeseries data.