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4 Weeks ago
Revalsys Technologies - Data Engineer - Machine Learning & Statistical Modeling
Bangalore, Karnataka, India
Information Technology
Full-Time
Revalsys Technologies
Overview
Responsibilities
- Gains a thorough understanding of the requirements and ensure that work product aligns with customer requirements.
- Works within the established development guidelines, standards, methodologies, and naming conventions Builds processes to ingest, process and store massive amount of data.
- Assists with optimization the performance of bigdata ecosystems.
- Wrangles data in support of data science projects Performs productionization of ML and statistical models for Data Scientists & Statisticians Develops, constructs, tests, and maintains scalable data solutions for structured and unstructured data to support reporting, analytics, ML and AI Assists with research and building of proof of concepts to test out theories recommended by Senior and Lead Data
- Staff Data Engineers work requires the application of a wide variety of established processes or Bachelor of Science in a related field preferred or working towards it
- A total minimum of 2 years of design and development Experience, Skills, and Knowledge:
- Knowledge of CRISP-DM methodology relevant to Data Engineering i.e: Data preparation and Deployment.
- Knowledge in Big Data technologies, concepts, and their applications for data processing.
- Advanced knowledge of Business Intelligence, Data Warehousing.
- Knowledge in fundamentals of Machine Learning and Artificial Intelligence using Microsoft technologies.
- Performance tuning and code optimization in SQL.
- Data profiling and dimension modeling techniques and creation of logical and physical data models.
- Experience working with job scheduling tools.
- Experience with SQL, Python, Linux Shell Scripting or MS Power Shell, Spark, NoSQL
- Experience with Cloud Service Models: PaaS, IaaS, SaaS.
- Any Cloud Vendor out of three will be fine (Azure/GCP/AWS).
- Strong communication skills with the ability to explain complex topics in a simple and understandable way to non-technical audiences
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