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Position title: Data Engineer
Location: Houston, TX (Only local candidates)
Duration: 6 to 12 months
Please Note: The candidate should be on your company W2.
We are seeking a highly skilled Data Engineer to support and evolve our DAISY platform, a custom internal data intelligence and workflow application. This role requires hands-on experience building scalable and secure data pipelines, with a strong emphasis on data security, caching strategies, and database management.
You’ll work on designing, implementing, and optimizing pipelines that process high-volume data from various sources, ensuring system reliability, performance, and security. Experience with Docker is essential, and Kubernetes knowledge is a strong plus.
Key Responsibilities:
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Design and maintain robust, secure, and high-performance data pipelines to support DAISY’s processing and analytics workflows
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Implement authentication and authorization mechanisms to secure data access and movement
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Work with both SQL and NoSQL databases to store and retrieve structured and unstructured data efficiently
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Apply data caching and optimization strategies to improve application responsiveness and system performance
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Build, deploy, and manage containerized applications using Docker
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Collaborate with developers, architects, and security teams to ensure system compliance and performance
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Troubleshoot pipeline and infrastructure issues in development and production environments
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Contribute to architecture discussions and make recommendations for technology direction
Required Qualifications:
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5+ years of experience in data engineering or backend data development
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Strong hands-on experience in building data pipelines (ETL/ELT) for high-volume systems
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In-depth knowledge of data security, authentication, and authorization strategies
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Experience with SQL databases (e.g., PostgreSQL, SQL Server) and NoSQL databases (e.g., MongoDB, Redis)
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Experience with caching strategies (e.g., Redis, Memcached)
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Proficient in Docker – building, managing, and deploying containers
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Familiarity with data processing frameworks (e.g., Apache Kafka, Spark) is a plus
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Experience with CI/CD tools and version control (e.g., Git/GitHub)
Preferred Qualifications:
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Experience with Kubernetes for orchestration of containerized workloads
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Background working on internal enterprise platforms or workflow systems
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Knowledge of data governance, encryption, and compliance best practices
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Strong scripting skills in Python, Bash, or similar
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Experience in the Oil & Gas industry is a plus
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