> Markdown version of [/jobs/ext/4255957-data-engineer](https://www.wearedevelopers.com/jobs/ext/4255957-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Robotics Technologies LLC - **Location:** Plano, TX, United States - **Contract:** Temporary contract - **Skills:** Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Apache HTTP Server, Continuous Integration, Information Engineering, Data Integration, Data Sharing, Python (Programming Language), Operational Databases, Aws Command Line Interface (CLI), Enterprise Data Management, Data Ingestion, Snowflake, Apache Spark, Agentic-AI, Data Lakes, Pyspark, AWS Data Analytics, Bitbucket, Terraform, Data Pipelines, Jenkins, Databricks - **Published:** October 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=32e58bfe12d45aeb ## About the Role * Strong hands-on Python programming experience. * Practical AWS experience with S3, AWS CLI, EC2, Glue, and Lake Formation. * Understanding of data lakes and data pipeline development. * Experience with CI/CD pipelines, Jenkins, and Bitbucket. * Ability to build, test, troubleshoot, and maintain production data pipelines. Preferred Skills: * Snowflake, Databricks, and Apache Iceberg. * AWS Bedrock and exposure to AI agentic workflows. * Terraform, PySpark, or Apache Spark. ## Description We are seeking a hands-on Data Engineer with strong Python and AWS experience to build and maintain data pipelines and connectivity solutions across enterprise data platforms. This role focuses on developing reliable data integrations and supporting data lake and lakehouse technologies rather than designing enterprise architecture., * Build and maintain data pipelines using Python and AWS services. * Develop data ingestion and connectivity solutions for enterprise data platforms. * Work with AWS data lake services, including S3, Glue, and Lake Formation. * Use AWS CLI and EC2 to support data engineering workloads. * Implement and maintain CI/CD pipelines using Jenkins and Bitbucket. * Troubleshoot data pipeline issues and improve data quality and reliability. * Collaborate with platform and engineering teams to support shared data services.