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

Artha; United States; 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

Python ETL data modeling SQL/NoSQL data warehousing

This job requires a minimum of 3 years of experience.

About the Job

Job Title: Big Data Engineer

Job Type: Contractor

Location: Remote

Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

Key Responsibilities:

• Design, build, and maintain scalable big data pipelines and architectures to support robust data solutions.
• Collaborate with cross-functional teams to understand and deliver on data requirements and business objectives.
• Implement data integration, transformation, and processing solutions using Python and relevant big data technologies.
• Develop, manage, and optimize distributed databases and storage systems for efficiency and reliability.
• Monitor, troubleshoot, and enhance data systems to ensure high availability and performance.
• Enforce data quality, security, and governance standards across all solutions.
• Document solutions and communicate complex technical concepts effectively, both in writing and verbally.

Required Skills and Qualifications:

• Proven expertise in big data engineering with hands-on experience building and maintaining large-scale data pipelines.
• Advanced proficiency in Python for data processing, automation, and integration.
• Deep understanding of relational and NoSQL databases, including optimization and management techniques.
• Experience with distributed data processing frameworks (e.g., Hadoop, Spark, Flink).
• Strong foundation in data modeling, ETL processes, and data warehousing principles.
• Excellent written and verbal communication skills, with the ability to convey technical ideas clearly to both technical and non-technical stakeholders.
• Detail-oriented, proactive, and self-motivated, thriving in remote and autonomous work environments.

Preferred Qualifications:

• Prior experience in fast-paced or startup-like environments supporting global teams.
• Expertise with cloud-based big data platforms (e.g., AWS, GCP, Azure).
• Familiarity with machine learning operations and data science workflows is a plus.

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