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# Big Data Engineer

- **Company:** RECRUITER LLC
- **Location:** United States (Remote available)
- **Salary:** $62,400.0 - $166,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Databases, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Systems, Data Warehousing, Database Design, Distributed Computing Environment, Distributed Data Store, Distributed Systems, Apache Hadoop, Python (Programming Language), Machine Learning, NoSQL, Systems Development Life Cycle, Systems Integration, Data Processing, Scripting, Google Cloud, Cloud Platform System, System Availability, Database Optimization, Apache Spark, Apache Flink, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/big-data-engineer--76f458e1-09f0-4bf8-9f1c-0341bd309fd6

## About the Role

Big Data, Big Data, Python Programming/Scripting Language, Data Processing, Distributed Computing, Data Quality, Artificial Intelligence (AI), Distributed Databases, Data Management, Scalable System Development, * Proven hands-on experience in big data engineering, including building and maintaining large-scale data pipelines.

 * Advanced proficiency in Python for data processing, automation, and system integration.
 * Strong understanding of relational and NoSQL databases, including database design, optimization, and administration.
 * Experience with distributed data processing frameworks such as Hadoop, Spark, or Flink.
 * Solid understanding of data modeling, ETL processes, and data warehousing principles.
 * Strong troubleshooting and analytical skills for diagnosing performance, reliability, and data-quality issues.
 * Ability to communicate complex technical concepts clearly to both technical and non-technical stakeholders.
 * Detail-oriented, proactive, and comfortable working independently in a remote environment., * Experience working in fast-paced or startup-like environments.
 * Experience collaborating with globally distributed teams.
 * Hands-on experience with cloud-based data platforms and services, including AWS, Google Cloud, or Microsoft Azure.
 * Familiarity with MLOps, machine learning infrastructure, or data science workflows.
 * Experience building data systems that support analytics, machine learning, or other high-volume data applications.

Who Should Apply

This role is suited to data engineers who have practical experience working with large-scale datasets and distributed data systems. You should be comfortable moving between pipeline development, database optimization, data processing, infrastructure reliability, and data governance.

Strong independent problem-solving and communication skills are important, particularly for engineers working remotely and across multidisciplinary teams.

## Description

This contract role is for a Big Data Engineer experienced in designing and maintaining large-scale data infrastructure, pipelines, and processing systems.

You'll apply your engineering expertise to build reliable data solutions and contribute high-quality technical input to the training and improvement of next-generation AI systems. The work involves large-scale data processing, Python development, distributed systems, databases, data quality, and governance.

What You'll Do

 * Design, build, and maintain scalable data pipelines and architectures for large-scale data environments.
 * Translate business and technical requirements into reliable data engineering solutions.
 * Develop data integration, transformation, and processing workflows using Python and appropriate big data technologies.
 * Build, manage, and optimize distributed databases and storage systems for performance, reliability, and scalability.
 * Monitor data infrastructure, troubleshoot failures, and improve system availability and performance.
 * Implement data quality, security, and governance practices across data pipelines and platforms.
 * Apply sound data modeling, ETL, and data warehousing principles to support reliable data systems.
 * Collaborate with technical and non-technical stakeholders to understand requirements and communicate implementation decisions.
 * Document architectures, processes, technical solutions, and operational procedures clearly.