| Location | Austin, TX |
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