We are seeking an experienced Software/Data Engineer to design and deliver scalable data processing systems and AI-enabled workflows. This contract role sits at the intersection of software engineering and data engineering, with a strong focus on Spark, cloud-based distributed processing, production reliability, and data preparation for analytics and machine learning use cases
Job DescriptionKey Responsibilities
- Design and develop scalable data processing solutions using Spark and Amazon EMR or comparable cloud-based data processing platforms.
- Build and maintain batch and distributed data pipelines.
- Develop software components for data transformation, feature preparation, and AI or machine learning workflow integration.
- Collaborate with engineering, AI, and product teams to operationalize data-driven and model-enabled use cases.
- Optimize data pipeline performance, cost efficiency, scalability, and production reliability.
- Troubleshoot data and application issues across development and production environments.
- Contribute to architecture discussions, technical documentation, and engineering standards.
- Ensure solutions align with data quality, governance, and security expectations.
Must-Have Skills
- 4+ years of software engineering or data engineering experience.
- Strong experience with Spark and distributed data processing.
- Experience with Amazon EMR or similar cloud-based data processing platforms.
- Proficiency in Java, Python, or a related programming language.
- Exposure to AI or machine learning workflows, model integration, or data preparation for intelligent systems.
- Strong understanding of scalable data architecture and performance optimization.
- Strong debugging and collaboration skills.
- Comfortable delivering in evolving, data-intensive environments.
- Ability to bridge software engineering and data engineering responsibilities.
- Strong execution focus with practical architecture judgment.
Nice-to-Have Skills
- Experience with Kafka, Airflow, data lakes, or data warehouse ecosystems.
- Familiarity with MLOps, feature stores, or AI platform integration.
- Experience with AWS-native services and observability tooling.
- Enterprise experience strongly preferred.
Required Tools & Platforms
- Apache Spark.
- Amazon EMR or a comparable cloud-based distributed data processing platform.
- Java, Python, or a related programming language.
Location, Time & Engagement
- Remote contract role.
- Candidates must be located in LATAM, excluding Mexico.
- U.S. Central Time coverage is required.
- Full-time allocation of approximately 40 hours per week.
- Current contract end date is March 31, 2027.
Skills Required
- 4+ years of software engineering or data engineering experience
- Strong experience with Apache Spark and distributed data processing
- Experience with Amazon EMR or a comparable cloud-based data processing platform
- Proficiency in Java, Python, or a related programming language
- Exposure to AI or machine learning workflows, model integration, or data preparation for intelligent systems
- Understanding of scalable data architecture and performance optimization
- Strong debugging and collaboration skills
- Ability to bridge software engineering and data engineering responsibilities
- Ability to work in evolving, data-intensive environments
- Strong execution focus and practical architecture judgment
- Experience with Kafka, Airflow, data lakes, or data warehouse ecosystems
- Familiarity with MLOps, feature stores, or AI platform integration
- Experience with AWS-native services and observability tooling
- Enterprise experience
- Must be located in LATAM, excluding Mexico
- U.S. Central Time coverage
- Full-time availability of approximately 40 hours per week
What We Do
Lifted, an Upwork Company, is a B2B SaaS platform that helps enterprise organizations source, contract, manage, and pay contingent talent globally and compliantly. It supports multiple engagement models, including independent contractors, staff augmentation, and employer-of-record, while integrating with MSPs and VMSs to provide centralized visibility, spend control, and audit-ready compliance, delivering a white-labeled talent experience for hiring managers and contingent workforce programs.
