Remote Cloud Data Engineer for AI/ML Pipelines

United States Digital Space LLC

United States

Remote

USD 117,096 - 130,872

Full time

14 days+

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Job summary

United States Digital Space LLC is recruiting a Cloud Engineer I – Data Engineering to design, build, and maintain secure cloud-based data platforms for VA initiatives. You will work in Agile teams to create scalable data pipelines, data lakes, and analytics environments supporting enterprise reporting, AI, and ML with strict security and privacy requirements.

The role emphasizes cloud-native solutions in Azure and AWS, IaC automation, and collaboration with data architects, developers,

Qualifications

4+ years of professional IT experience, including 2+ years developing cloud-based data engineering solutions.
Experience building enterprise ETL/ELT pipelines.
Experience supporting Microsoft Azure and/or AWS cloud environments.
Experience working with relational and non-relational databases.
Experience supporting federal government or highly regulated environments.
Experience using Git-based source control.

Responsibilities

Design, develop, and maintain cloud-native data engineering solutions within Microsoft Azure and AWS environments.
Develop, deploy, and optimize ETL/ELT pipelines for structured and unstructured data.
Build and maintain scalable cloud-based data lakes, data warehouses, and data marts.
Integrate data from multiple enterprise systems using APIs, databases, flat files, and cloud-native integration services.
Develop Infrastructure-as-Code (IaC) to automate cloud infrastructure deployment and management.
Optimize data ingestion, transformation, and storage processes for performance and scalability.
Implement data quality, validation, cleansing, and monitoring processes.
Support cloud-based analytics, business intelligence, and AI/ML initiatives.
Develop automation scripts to improve operational efficiency.
Collaborate with data architects, application developers, business analysts, and cybersecurity personnel.
Monitor cloud infrastructure and data platform performance, availability, and costs.
Support backup, disaster recovery, and business continuity planning for cloud data environments.
Produce technical documentation, including: Data flow diagrams, Data dictionaries, ETL specifications, Architecture diagrams, SOPs.