Remote | Data Engineer — $140,000–$180,000/year
About The Position
24-MAG LLC is sharing a full-time, fully remote opportunity for an experienced Data Engineer to build and operate infrastructure supporting AI-driven products and research initiatives. The role focuses on designing and scaling distributed data pipelines, managing large datasets across cloud environments, and building reliable systems for analytics, experimentation, and model development. The engineer will work with Python, SQL, Apache Spark, AWS, distributed data processing, and scalable data architecture. Responsibilities span data ingestion, transformation, storage, monitoring, and operational reliability. The role may support AI/ML experimentation, model-development workflows, and LLM-related data infrastructure. Data volumes, infrastructure requirements, and technical priorities may evolve as products and research initiatives scale. This opportunity is available through 24-MAG LLC, which connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
Requirements
- Strong professional experience in data engineering or distributed data systems.
- Advanced proficiency in Python and SQL.
- Hands-on experience with Apache Spark or comparable distributed-processing frameworks.
- Strong experience with AWS data services and cloud-native architecture.
- Experience with SQL and NoSQL databases.
- Demonstrated experience processing large-scale datasets.
- Strong understanding of partitioning, performance optimization, and scalable architecture.
- Familiarity with orchestration, automation, monitoring, and data-quality workflows.
Nice To Haves
- Exposure to AI/ML or research environments.
- Familiarity with LLM training, evaluation, or experimentation datasets.
- Experience with data-visualization tools such as Matplotlib, Seaborn, or Plotly.
Responsibilities
- Design, build, and maintain large-scale pipelines for structured and unstructured data.
- Develop distributed processing workflows using Apache Spark or comparable frameworks.
- Optimize transformations, partitioning strategies, and computational workloads.
- Identify and resolve performance bottlenecks across high-volume data systems.
- Support downstream analytics, experimentation, and model-development requirements.
- Design scalable AWS-based data architectures across SQL and NoSQL systems.
- Build reliable data ingestion, transformation, storage, and distribution workflows.
- Write efficient Python and SQL for production data processing.
- Evaluate storage and database technologies against workload requirements.
- Improve scalability, maintainability, accessibility, and operational efficiency.
- Implement monitoring, validation, and automation across data workflows.
- Identify failures, anomalies, and data-quality issues.
- Maintain integrity and reliability throughout pipelines and storage layers.
- Collaborate with AI researchers, data scientists, and engineering teams.
- Support data infrastructure for AI/ML training, evaluation, and experimentation.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
