Big Data Engineer (Azure)

Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

The Big Data Azure Engineer will be responsible for architecting, designing, and implementing advanced analytics capabilities. These capabilities include batch and streaming analytics, machine learning models, natural language generation, and other emerging technologies in the field of advanced analytics.

Requirements

• Bachelor’s degree in Computer Science or similar field
• 4+ years of experience in traditional and modern Big Data technologies (HDFS, Hadoop, Hive, Pig, Sqoop, Kafka, Apache Spark, hBase, Oozie, No SQL databases)
• Experience in Java/Python/Scala
• Experience extracting/querying/joining large data sets at scale
• Experience building data platforms using Azure stack
• Experience building data ingestion pipelines using Azure Data Factory to ingest structured and unstructured data
• Strong knowledge on Azure Storage schematics such as Gen1 and Gen2
• Experience in harmonizing raw data into a consumer-friendly format using Azure Databricks
• Knowledge of Azure networking, security, key vaults, etc.
• Experience in data wrangling, advanced analytic modeling, and AI/ML capabilities is preferred
• Experience utilizing Snowflake to build data marts with the data residing in Azure storage is a plus
• Strong communication and organizational skills

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

Originally posted on Himalayas