We are seeking a highly skilled Big Data Engineer to design, build, and optimize scalable data platforms that support enterprise analytics, reporting, machine learning, and business intelligence initiatives. The ideal candidate will have experience developing large-scale data pipelines, managing cloud-based data ecosystems, and implementing high-performance data solutions that enable data-driven decision-making across the organization. Key Responsibilities Design, develop, and maintain large-scale data processing systems and data pipelines. Build and optimize ETL/ELT workflows for structured, semi-structured, and unstructured data sources. Develop scalable solutions using distributed computing frameworks such as Apache Spark, Hadoop, and Kafka. Collaborate with Data Scientists, Analysts, Architects, and business stakeholders to support analytical and operational requirements. Integrate data from multiple internal and external systems while ensuring data quality and integrity. Design and implement cloud-native data solutions within AWS, Azure, or Google Cloud environments. Monitor, troubleshoot, and improve the performance, reliability, and scalability of data platforms. Implement data governance, security, compliance, and access control standards. Support real-time and batch data processing requirements. Document architectures, workflows, and technical specifications to ensure operational excellence.
Required Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field. 5+ years of experience in data engineering, big data, or related technical roles. Strong proficiency in SQL and Python, Scala, or Java. Experience with distributed data processing technologies: Apache Spark Hadoop Hive Kafka Airflow Experience building ETL/ELT pipelines and data integration solutions. Strong understanding of data warehousing concepts and dimensional data modeling. Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. Knowledge of CI/CD processes, Git, and DevOps methodologies. Excellent analytical, troubleshooting, and problem-solving skills. Strong communication and collaboration abilities. Preferred Qualifications Experience with Databricks, Snowflake, Redshift, BigQuery, or Azure Synapse. Familiarity with containerization technologies including Docker and Kubernetes. Experience supporting Machine Learning and AI workloads. Knowledge of data governance, metadata management, and master data management. Relevant cloud or data certifications, such as: AWS Certified Data Engineer Microsoft Azure Data Engineer Associate (DP-203) Google detail oriented Data Engineer Databricks Data Engineer Certification
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- Memphis, TN
- onsite
- Temporary / Contract
- 70 - 85 USD / Hourly
- We are seeking a highly skilled Big Data Engineer to design, build, and optimize scalable data platforms that support enterprise analytics, reporting, machine learning, and business intelligence initiatives. The ideal candidate will have experience developing large-scale data pipelines, managing cloud-based data ecosystems, and implementing high-performance data solutions that enable data-driven decision-making across the organization. Key Responsibilities Design, develop, and maintain large-scale data processing systems and data pipelines. Build and optimize ETL/ELT workflows for structured, semi-structured, and unstructured data sources. Develop scalable solutions using distributed computing frameworks such as Apache Spark, Hadoop, and Kafka. Collaborate with Data Scientists, Analysts, Architects, and business stakeholders to support analytical and operational requirements. Integrate data from multiple internal and external systems while ensuring data quality and integrity. Design and implement cloud-native data solutions within AWS, Azure, or Google Cloud environments. Monitor, troubleshoot, and improve the performance, reliability, and scalability of data platforms. Implement data governance, security, compliance, and access control standards. Support real-time and batch data processing requirements. Document architectures, workflows, and technical specifications to ensure operational excellence.
- 2026-11-06T00:00:00Z