Remote Data Engineer — AI Data Pipelines (Spark, AWS)

Artha Nexgen; Northern (KY); Hybrid; USD 100,000 - 150,000; Full time

Benefits offered by this job

Equity compensation

Health insurance reimbursement

401(k) with match

Remote-first workforce

Job summary

Artha Nexgen is seeking a Data Engineer to build and scale data infrastructure powering AI-driven products and research. You will develop distributed data pipelines, manage large-scale datasets across cloud environments, and design reliable data systems for data processing, experimentation, and model development at scale.

Responsibilities include designing and maintaining scalable pipelines, optimizing Spark workflows in AWS, building storage across SQL and NoSQL, writing Python and SQL,

Qualifications

• Minimum 3 years of experience in data engineering.
• Experience building scalable data pipelines and data infrastructure for AI/ML workloads.
• Strong proficiency in Python and SQL, with distributed processing expertise.

Responsibilities

• Design, build, and maintain scalable data pipelines to ingest, process, and transform large-scale datasets from multiple sources.
• Develop and optimize distributed data processing workflows using Spark and cloud-native technologies.
• Build and maintain data storage solutions across SQL and NoSQL systems, ensuring scalability and performance.
• Design and implement data architectures on AWS to support high-volume data ingestion and distribution.
• Write efficient Python and SQL code to extract, transform, validate, and analyze large datasets.
• Ensure data quality, integrity, monitoring, and reliability across pipelines and storage.
• Collaborate with AI researchers, data scientists, and engineering teams to support data-intensive applications.
• Implement automation, orchestration, and monitoring workflows to support scalable data operations.

Skills

Python

SQL

Apache Spark

Distributed data processing

Tools

AWS data services

SQL/NoSQL databases

Job description

Artha Nexgen is seeking a Data Engineer to build and scale data infrastructure powering AI-driven products and research. You will develop distributed data pipelines, manage large-scale datasets across cloud environments, and design reliable data systems for data processing, experimentation, and model development at scale.

Responsibilities include designing and maintaining scalable pipelines, optimizing Spark workflows in AWS, building storage across SQL and NoSQL, writing Python and SQL,