Big Data Engineer

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.

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.

• Annual compensation equivalent: $62,400-$166,400
• Hourly rate: $30-$80/hour
• Annual equivalent is calculated at 40 hours per week × 52 weeks per year.
• Actual earnings will depend on hours worked and the duration of the contract.

Work Arrangement

• Contract position
• Fully remote
• Work focused on large-scale data engineering, distributed processing, databases, pipelines, and data infrastructure
• Collaboration with cross-functional technical and business stakeholders