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# Data Engineer

- **Company:** Value Proposition Enterprises, Inc.
- **Location:** Texas City, TX, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Software Applications, Automation of Tests, BigQuery, Cloud Computing, Cloud Database, Databases, Continuous Integration, Couchbase Servers, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Flow Control, Python (Programming Language), PostgreSQL, Oracle Databases, Oracle (Applications), Cloud Services, SQL Databases, Data Streaming, Pinecone, Google Cloud, Sql Optimization, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Google Vertex AI, Generative AI, Agentic-AI, Git, Pgvector, CrewAI, AutoGen, Apache Kafka, Data Management, FAISS, Restful APIs, Stream Processing, Data Pipelines, Docker, ChromaDB, Microservices
- **Published:** November 4, 2026
- **Apply:** https://www.dice.com/job-detail/8a7dd9bf-c6cb-43a3-9c7d-a775b2ef7ce4

## About the Role

We are looking for a highly skilled Senior Data Engineer with strong hands-on 10years experience in Python, SQL, AWS, Google Cloud Platform, database engineering, and Generative AI technologies., A hands-on, full-stack Data Engineer capable of delivering end-to-end solutions across data engineering, database modernization, cloud platforms, and GenAI-powered applications.

## Description

The ideal candidate will build scalable data platforms, develop cloud-native solutions, modernize enterprise databases, and deliver AI-powered applications using modern GenAI frameworks.

Mandatory Technical Skills

Python & Advanced SQL
ETL/ELT & Data Pipeline Development
AWS: RDS, S3, Glue, Lambda, Redshift, Bedrock
Google Cloud Platform: BigQuery, Cloud SQL, Dataflow, Pub/Sub, Vertex AI
Databases: Oracle, PostgreSQL, Couchbase
Apache Spark, Kafka & Airflow
REST APIs & Microservices
Batch, Streaming & Real-Time Data Processing
Docker, Git, CI/CD & Automation

GenAI / Agentic AI Skills

Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
LangChain & LangGraph
CrewAI & AutoGen
Agentic AI Architecture
Vector Databases: Pinecone, FAISS, Chroma, pgvector
AWS Bedrock & Google Vertex AI

Key Responsibilities

Design and develop scalable, cloud-native data engineering solutions.

Build and optimize batch, streaming, and real-time data pipelines.

Develop Python-based ETL/ELT workflows and microservices.

Design, migrate, and optimize Oracle, PostgreSQL, and Couchbase databases.

Implement GenAI-powered applications using LLMs, RAG, and Agentic AI frameworks.

Develop secure and scalable integrations across AWS and Google Cloud Platform.

Implement CI/CD, monitoring, automated testing, and data governance best practices.