Title: Data Solutions Analyst - Generative AI Location: Charlotte, NC Pay: $70/hr Type: 12-18 month W2 Contract Day to Day As a Data Solutions Analyst, you will play a key role in designing and implementing large-scale data architectures and platforms. This is a mid-level, yet impactful role suited for someone with 3-5 years of experience who wants to be hands-on, learn continuously, and grow into emerging technologies like Generative AI. You will work on end-to-end data solutions, integrating various data systems, and contributing to the development of a robust data fabric and data management ecosystem. Key Responsibilities: • Design, develop, and implement scalable big data architectures and data platforms that support analytics, AI, and data-driven decision making. • Lead end-to-end data integration, including data ingestion, transformation, and orchestration across diverse systems. • Build and maintain data pipelines, ensuring data quality, lineage, and governance (using tools like Calibra). • Develop and optimize data transformation processes with Apache Spark and Python for large-scale data processing. • Create interactive data visualizations and dashboards using JavaScript. • Collaborate with cross-functional teams to understand data requirements and deliver integrated data solutions. • Support data cataloging, data lineage, and data management initiatives, ensuring data is discoverable, trustworthy, and compliant. • Explore and grow knowledge in emerging AI and Generative AI (GenAI) technologies, understanding their potential applications. • Stay current with industry best practices and contribute to continuous improvement of data architecture and platform. Must Haves: • 3-5 years exp in understanding and working with Data Platforms and Data Applications. • Experience hands-on developing actual prototypes • Proficient in GenAI frameworks such as Langchain, LamaIndex, OpenAI's Assistants API, FAISS, RAG (Retrieval-Augmented Generation), Azure OpenAI Service, Azure Cognitive Search, and vector databases like Pinecone, VectorDB, AstraDB, and Azure Cosmos DB for MongoDB vCore. • Proficient in data modeling methodologies including dimensional modeling (Star and Snowflake schemas), Data Vault, 3NF, Kimball and Inmon approaches, and designing slowly changing dimensions (SCDs) for tracking historical data changes. • Expertise in Big Data tools including Hive for SQL-like queries on Hadoop, Pig for data flow scripting, Flink for stateful computations, Storm for real-time stream processing, MapReduce programming paradigm, Kafka for distributed event streaming, and PySpark for Python-based Spark programming, enabling efficient processing of large-scale datasets. • Experience designing and building big data architectures and data platforms. • Strong proficiency in Python for data processing and scripting. • Practical experience with Apache Spark for large-scale data transformation. • Ability to develop data visualizations and dashboards with JavaScript. • Familiarity with data pipeline management, data cataloging, data lineage, and data quality tools Plusses • Knowledge of graph processing and graph stores is a plus. • Basic understanding of Javaspringboot is good, but not a requirement. • Exposure to open data sources and cloud platforms is a huge plus. • Collibra experience