Java Spark Developer

Overture Rede; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Overture Rede is seeking a remote senior data platform engineer with extensive Java experience and AWS expertise to design and operate scalable data pipelines. You will work on distributed systems and data processing at scale.

The role emphasizes microservices, Spark-based processing, SQL querying, and cloud services like Glue, EMR, and Kinesis within AWS.

Qualifications

• 8+ years of experience in building complex data platforms and data engineering solutions.
• Hands-on experience with AWS services for data solutions.

Responsibilities

• Design, build and operate scalable data platforms and data pipelines.
• Develop microservices and data processing workflows using Spark and AWS.

Skills

Advanced Java

Microservices

Spring Boot

SQL Queries

AWS

Java 4-5 yrs

Spark

Scala/Java/Python

Tools

DynamoDB

DocumentDB

MongoDB

AWS Glue

EMR

Lambda

SQS

SNS

Kinesis

Job description

This is a remote position.

Key Points - Must Have

• Advanced Java proficient


• Microservices


• Spring boot


• Writing SQL Queries (proficient)


• AWS


• 4-5 Yrs in Java, 2-3 exp - Spark

Good to have

• Unix Shell scripting JD

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

• Experience with big data technologies such as: Spark, EMR, Hadoop, Hive,


• Experience programming with at least one modern language such as Scala, Java, Python


• Hands on experience on NoSQL DBs like DynamoDB, DocumentDB, MongoDB


• Hands on experience on implementing AWS Glue, EMR, Lambda functions, SQS, SNS,


• Experience on Real-time data ingestion and processing in AWS especially using services like AWS Kinesis


• AWS certification is preferred


• Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets


• Experience with data m

Originally posted on Himalayas