Advanced Java proficiency with 4-5 years in Java, 2-3 years in Spark, plus experience in microservices and Spring Boot.
8+ years of experience in building complex Data Platforms and Data Engineering solutions.
6+ years hands-on experience in architecture and development of data solutions in AWS.
Experience with big data technologies (Spark, EMR, Hadoop, Hive) and NoSQL databases (DynamoDB, DocumentDB, MongoDB); plus real-time data ingestion with AWS Kinesis.
Requirements:
Design, develop, and operate scalable data platforms and ETL/ELT pipelines on AWS using Spark, EMR, Hive, and related tools.
Architect and implement data solutions in AWS with services such as AWS Glue, EMR, Lambda, SQS, and SNS, including real-time ingestion with AWS Kinesis.
Develop Java-based microservices (Spring Boot) and ensure efficient SQL queries and data access patterns.
Collaborate with cross-functional teams to deliver robust, highly available, distributed data extraction, ingestion, and processing of large data sets, with emphasis on data quality and governance.
Job description
This is a remote position.
Key Points -
Must Have: â Advanced Java proficient â Microservices â Spring boot â Writing SQL Queries (proficient) â AWS â 4-5 Yrs in Java, 2-3 exp - Spark â Good to have â Unix Shell scripting
JD â Minimum of 8 years of experience in building complex Data Platforms and Data Engineering solutions Minimum of 6 years of hands on experience in architecture and development of data solutions in AWS environment using AWS Services â Experience with big data technologies such as: Spark, EMR, Hadoop, Hive, â Experience programming with at least one modern language such as Scala, Java, Python â Hands on experience on NoSQL DBs like DynamoDB, DocumentDB, MongoDB â Hands on experience on implementing AWS Glue, EMR, Lambda functions, SQS, SNS, â Experience on Real-time data ingestion and processing in AWS especially using services like AWS Kinesis â AWS certification is preferred â Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets â Experience with data m
