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# Big Data Engineer

- **Company:** Aivra Health Llc
- **Location:** San Jose, CA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Big Data, Information Engineering, Data Integrity, Extract Transform Load (ETL), Data Warehousing, Distributed Computing Environment, Apache Hadoop, Apache Hive, Python (Programming Language), Machine Learning, Standard Sql, Unstructured Data, Data Storage Technologies, Cloud Platform System, Snowflake, Apache Spark, Git, Kubernetes, Information Technology, Apache Kafka, Data Pipelines, Docker, Databricks
- **Published:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/8e5342dd-9324-426d-a0a0-1edd49fddd50

## About the Role

* Bachelor''s degree in Computer Science, Information Technology, Data Engineering, or a related field.
 * Strong knowledge of SQL and Python or Scala.
 * Experience with distributed data processing frameworks.
 * Understanding of ETL/ELT concepts and data warehousing.
 * Strong analytical, problem-solving, and communication skills.

Preferred Skills

 * Apache Spark
 * Hadoop
 * Kafka
 * Hive
 * Scala
 * Python
 * SQL
 * Databricks
 * Snowflake
 * Apache Airflow
 * AWS
 * Docker
 * Kubernetes
 * Git

## Description

We are seeking a highly skilled Big Data Engineer to design, develop, and maintain scalable big data solutions that process large volumes of structured and unstructured data. You will collaborate with Data Engineers, Data Scientists, and Software Engineers to build reliable data pipelines and support advanced analytics initiatives., * Design, build, and maintain scalable big data pipelines.

 * Develop ETL/ELT workflows for large-scale data processing.
 * Optimize data storage, retrieval, and processing performance.
 * Work with distributed computing frameworks and cloud-based data platforms.
 * Integrate data from multiple sources into enterprise data lakes and warehouses.
 * Monitor data quality and ensure data integrity.
 * Collaborate with cross-functional teams to support business intelligence and machine learning initiatives.
 * Troubleshoot and resolve data pipeline and infrastructure issues.
 * Maintain technical documentation and follow best engineering practices.


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