IT - Technology Architect | DevOps | Continuous integration - Java

Cloudspace LLC

  • Austin, TX
  • 4 days ago

    Highlights

    We are looking for a skilled Software Engineer with strong hands-on experience in Java, MongoDB, and Apache Spark to design, build, and optimize large-scale data processing systems and backend services. You'll work on high-throughput applications that process and analyse large datasets, collaborating closely with data engineering, product, and platform teams.

    Numbers & Facts

    LocationAustin, TX

    Description

    Client: Apple



    Do you have a pre-identified candidate? New sourcing? Existing Subcon?- New sourcing



    Max Hourly Pay Rate to the candidate?: $ 75/Hr



    Do you have a preferred interview schedule?: Yes



    Would you require the candidates to meet you for an in-person interview?: No



    Is Skype/WebEx interview, OK?: Yes



    Work Location with ZIP: Austin-TX-78753


    Is remote work an option?: No



    If remote work, please indicate how many days are required to be in the office and remote: 3 days in a week required to be in the office



    Chance to be perm?: Yes



    Performance Expectations:



    Technical Hiring Criteria (Must Haves)



    Top 3 Required skills: Java, MongoDB, and Apache Spark

    Years of experience in each of the must-have skills: 8+ Years

    Any Certifications required: No



    Any additional information you would like to share about the project specs/nature of work: NA



    Job Description -
    We are looking for a skilled Software Engineer with strong hands-on experience in Java, MongoDB, and Apache Spark to design, build, and optimize large-scale data processing systems and backend services. You'll work on high-throughput applications that process and analyse large datasets, collaborating closely with data engineering, product, and platform teams.
    Key Responsibilities
    - Design, develop, and maintain scalable backend services and APIs using Java (Spring Boot / Java 8+).
    - Build and optimize batch and streaming data pipelines using Apache Spark (Spark SQL, Spark Streaming/Structured Streaming).
    - Design efficient schemas, indexes, and aggregation pipelines in MongoDB for high-volume read/write workloads.
    - Optimize query performance and troubleshoot bottlenecks across Java services, Spark jobs, and MongoDB collections.
    - Integrate Spark jobs with MongoDB (via MongoDB Spark Connector) and other data sources (Kafka, HDFS, S3, etc.).
    - Write clean, well-tested, maintainable code following best practices (unit/integration testing, code reviews, CI/CD).
    - Collaborate with cross-functional teams to translate business requirements into technical solutions.
    - Monitor, debug, and improve reliability and performance of production data pipelines and services.
    - Participate in architecture discussions and contribute to technical design decisions.

    Project Code: FY26 Q4 Project code for ETL Platform Su

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