🌎 Data Engineer, Remote - Full Time

Xperteez Technology•,

13h•$140,000 - $180,000•Remote

About The Position

We are looking for a Data Engineer to build and scale the data infrastructure that powers AI-driven products and research initiatives. In this role, you will develop distributed data pipelines, manage large-scale datasets across cloud environments, and design reliable data systems that support data processing, experimentation, and model development at scale.

Requirements

Strong proficiency in Python, SQL, and distributed data processing frameworks such as Apache Spark.
Hands-on experience with AWS data services and cloud-native data architectures.
Experience working with both SQL and NoSQL databases.
Experience managing and processing large-scale datasets in distributed environments.
Strong understanding of data partitioning, performance optimization, and scalable data architectures

Nice To Haves

Exposure to AI/ML workflows or research environments.
Experience with data visualization tools such as Matplotlib, Seaborn, or Plotly.
Familiarity with LLM-related data workflows (datasets for training, evaluation, or prompt experimentation).

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, performance, and reliability.
Design and implement data architectures on AWS to support high-volume data ingestion, processing, and distribution.
Write efficient Python and SQL code to extract, transform, validate, and analyze large datasets.
Ensure data quality, integrity, monitoring, and operational reliability across data pipelines and storage layers.
Collaborate with AI researchers, data scientists, and engineering teams to support data-intensive applications and experimentation.
Implement automation, orchestration, and monitoring workflows to support scalable and efficient data operations.