Job description

Role: Azure Data Engineer Location: Princeton, NJ (Onsite) Primary Skill: Azure Data Factory, Azure Databricks, Gen AI- LMM (Large Multi-Modal Models) and RAG (Retrieval-Augmented Generation) No. of years experience: 12+ years Note: Only on W2 Skillset and Experience • Overall, 3-5 years of skills with Data Migration and Azure Cloud Data Migration and Data Warehousing related projects. • Collaborate with AI/ML teams to build and operationalize Generative AI pipelines within Databricks. • Work with LLMs (Large Language Models) and vector databases for text summarization, retrieval-augmented generation (RAG), and data enrichment. • Use Databricks MosaicML, MLflow, or Hugging Face integrations for training and deploying custom AI models. • Strong experience in design and configuration of data movement and transformation (ETL) technologies such as Azure Data Factory • Hands on experience on Azure Cloud and its Native components like Azure Data bricks • Strong experience in Data storage technologies such as Azure SQL DW, Azure Data Lake • Strong experience in analytics solutions using Databricks, Azure Synapse Analytics • String experience in coding with languages like SQL, Pl/SQL • Good-to-have Azure certification • Good experience in Requirements gathering, Design & Development • Working with cross-functional teams to meet strategic goals. • Experience in high volume data environments • Critical thinking and excellent verbal and written communication skills • Strong problem solving and analytical abilities, should be able to work and delivery individually • Good knowledge of data warehousing concepts • Good communication skills • Worked in agile methodologies and waterfall execution models • Must have excellent oral and written communication with need to interact clients directly • Must be both individual contributor, good team player and self-motivated to take on challenges Roles and Responsibilities • Provide technical and development support to client to build and maintain data pipeline • Develop data mapping documents listing business and transformational rules • Develop, unit test, deploy and maintain data pipelines • Analyse source specifications and build data mapping documents • Identify and document applicable non-functional requirements • Understand profiling results and validate data quality rules • Utilize data analysis tools to construct and manipulate datasets to support analyses • Collaborate with and support Quality Assurance (QA) in building functional scenarios and validating results

Requirements