Python Data Engineer

Support here with 3 good profiles for the below requirement before 12pm Today.

Client : Visionet
Skills: Strong in Python development

Responsibilities Work side-by-side with machine learning engineers to solve AI problems
Provide expertise in ETL, data wrangling, data analysis and feature engineering using complex big data from a wide variety of sources
Create and maintain an optimal, scalable, and automated big data pipeline from inception to delivery, augmenting a broader machine learning pipeline
Implement feature engineering as part of a machine learning model training workflow
Support the development of proofs of concept to demonstrate the application of AI/Client capabilities in solving customer problems in collaboration with product and other development teams Collaborate effectively with other teams
A self-starter who can align the technology solution with a business goal Qualifications
Minimum of a Bachelor's degree in Computer Science, Engineering, Mathematics or equivalent
3+ years' experience in supporting the development of production-ready solutions leveraging data engineering
Strong hands-on expertise in Python and open-source libraries and tools such as NumPy, SciPy, scikit-learn, pandas, matplotlib, anaconda, Jupyter, etc
. Experience building, optimizing, and scaling data pipelines, architectures, and data sets in cloud-based (AWS or Azure) environments
Advanced working SQL and NoSQL knowledge, experience working with relational databases (e.g., RDS, Oracle, Postgres) and document databases (e.g., DynamoDB, Cosmos DB)
Experience with cloud data warehouses (e.g., Redshift, Azure SQL Data Warehouse) and search engines (e.g., Solr, Elasticsearch)
Working knowledge of message queuing, stream processing, and highly scalable data stores
Experience with tools such as Kafka, Spark, or Hadoop is a plus
Apart from Python, working knowledge of languages such as Java, C++, or C#
Experience working with agile and Software Development Lifecycle tools (e.g., JIRA, Confluence, Git

Mode of work: Hybrid