Job Description
The Junior Data & AI Engineer will support data pipelines, cloud-based data solutions, and AI-enabled applications, including data preparation for connectors and reusable data products. The role requires strong programming and SQL fundamentals, hands-on exposure to Snowflake, and an interest in emerging AI technologies. The engineer will work under guidance from experienced colleagues; practical experience and willingness to learn are valued over expertise in every listed technology.
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Key Responsibilities:
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· Develop and maintain data pipelines, SQL queries, and transformations using Snowflake and Python; support data integration, modeling, and ETL/ELT development.
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· Prepare, clean, and validate data for AI connectors and help build reusable data products, including curated datasets and documented views.
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· Assist in building AI/GenAI applications using LLMs, APIs, and retrieval-based approaches, with exposure to relational, graph, and vector data technologies.
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· Collaborate on development, testing, debugging, and documentation. Use approved AI coding tools while understanding and validating generated code and outputs.
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· Follow Firm requirements for confidentiality, data access, and approved AI use; escalate data-quality or technical issues when guidance is needed.
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Required Skills:
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· Python: Hands-on experience with data processing, automation, Pandas, and basic API integration.
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· SQL & Snowflake: Practical exposure to SQL queries, tables and views, data transformations, and ETL/ELT concepts.
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· AI / GenAI: Basic understanding of LLMs, prompting, retrieval-augmented generation (RAG), embeddings, and vector search.
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· Development Fundamentals: Familiarity with REST APIs, Git, JSON, testing, and documentation.
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· Strong attention to detail, analytical thinking, communication, and willingness to learn and seek guidance.
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Nice to Haves:
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· Exposure to graph data models, knowledge graphs, Neo4j, Cypher, or similar technologies.
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· Exposure to LangChain, LlamaIndex, Snowpark, Snowflake Cortex AI, or other cloud AI services.
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· Familiarity with AWS, Azure, or GCP and AI-assisted coding tools such as GitHub Copilot.
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Education & Experience:
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· Bachelor's degree in a relevant technical field or equivalent practical experience.
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· 0–2 years of relevant experience, including internships or academic projects. Strong fundamentals and practical project experience are valued over expertise in every listed technology.