{"schemaVersion":"jobsearcher.job.v1","id":"01cebb6e191e085ca0171902","url":"https://jobsearcher.com/jobs/01cebb6e191e085ca0171902","canonicalUrl":"https://jobsearcher.com/jobs/01cebb6e191e085ca0171902","title":"GenAI Data Engineer","description":"Overview\r\nThe Data Engineer works with moderate supervision across two equally weighted domains including large scale data pipeline development processing market events in a cloud environment and design and development of agentic AI systems including LLM powered regulatory data assistants, MCP servers, and agent harness architectures. This role is heavily focused on building GenAI tools and intelligent agents while remaining hands on with Python, SQL, and big data technologies. The engineer will support both proof of concept initiatives and production grade systems, helping scale solutions across the organization. This position contributes to overall product quality throughout the software development lifecycle and operates within a highly regulated environment that requires strong security, governance, and auditability. The ideal candidate is a hybrid engineer who understands both large scale data platforms and modern LLM based systems and can translate ideas into practical, reusable solutions.\r\nResponsibilities\r\nBuild and maintain ETL and ELT pipelines using Apache Spark, Hive, and Trino across S3 based data lake environments\r\nDevelop and optimize SQL for large scale datasets including window functions, multi table joins, and complex aggregations\r\nBuild and engineer big data systems using EMR on EC2 and EMR on EKS and develop solutions on analytical platforms such as SageMaker, Domino, and Dataiku\r\nParticipate in data quality monitoring, anomaly detection, and production incident investigation\r\nBuild and productionize LLM powered agents using AWS Bedrock and agent frameworks such as LangChain, LangGraph, AWS Strands or similar\r\nDesign agent harness architectures that combine LLM reasoning with deterministic execution including RAG based SQL generation and structured output validation\r\nImplement agent memory, context management, and tool integration including MCP servers, APIs, and data catalog lookups\r\nBuild evaluation frameworks for agent accuracy including paraphrase robustness, routing precision, and structural consistency\r\nStay informed of advances in LLM frameworks and emerging AI capabilities\r\nSupport proof of concept AI initiatives and scale successful solutions across teams\r\nWrite clean, well tested code and contribute to CI and CD pipelines and infrastructure as code on AWS\r\nEnsure secure handling of sensitive regulatory data with auditable execution traces across both data pipelines and AI outputs\r\nPartner across teams, communicate technical concepts clearly, and maintain documentation\r\nActively learn from senior engineers and contribute to continuous improvement of processes and engineering practices\r\nQualifications\r\nBachelor degree in Computer Science, Data Science, Information Systems or related discipline with at least two years of experience or equivalent work experience\r\nStrong experience building data pipelines using Apache Spark and SQL\r\nExperience with SQL query engines such as Hive and Trino and cloud data platforms including AWS S3, EMR, and Lambda\r\nStrong understanding of large scale data challenges such as data skew, high volume processing, and troubleshooting job failures\r\nHands on experience building LLM powered agent systems that use tools and produce structured outputs\r\nExperience with at least one agent framework such as LangChain, LangGraph, or AWS Strands\r\nKnowledge of prompt engineering, RAG architectures, and context and memory management\r\nExperience working with foundation model APIs such as Anthropic Claude, Amazon Nova, or OpenAI\r\nUnderstanding of agent memory models including working memory, episodic memory, and semantic memory\r\nFamiliarity with agent harness design including guardrails, routing, verification loops, and graceful degradation\r\nHands on experience using AI development tools such as GitHub Copilot, Q Developer, ChatGPT, or Claude\r\nExperience integrating AI into development workflows including code generation, debugging, and testing\r\nStrong experience with AWS services including S3, EMR, Lambda, Bedrock, and Step Functions\r\nExperience using S3 with Spark including file formats and consistency considerations\r\nFamiliarity with AWS monitoring and logging tools such as CloudWatch and CloudTrail\r\nProficiency in Python for data engineering and automation with strong understanding of clean code, modular design, and performance\r\nStrong SQL skills including window functions, joins, aggregations, and handling edge cases such as null values and duplicates\r\nStrong understanding of collections, concurrency, and memory management\r\nExposure to containerization and orchestration technologies such as Docker and Kubernetes\r\nExperience with infrastructure as code and CI and CD pipelines\r\nStrong communication skills and ability to work in a fast paced environment\r\nAbility to quickly learn new technologies and adapt to evolving requirements\r\nJ-18808-Ljbffr","company":"Tential Solutions","rawCompany":"tential solutions","city":"Vernon","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-25T00:50:16.607Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"GenAI Data Engineer","description":"Overview\r\nThe Data Engineer works with moderate supervision across two equally weighted domains including large scale data pipeline development processing market events in a cloud environment and design and development of agentic AI systems including LLM powered regulatory data assistants, MCP servers, and agent harness architectures. This role is heavily focused on building GenAI tools and intelligent agents while remaining hands on with Python, SQL, and big data technologies. The engineer will support both proof of concept initiatives and production grade systems, helping scale solutions across the organization. This position contributes to overall product quality throughout the software development lifecycle and operates within a highly regulated environment that requires strong security, governance, and auditability. The ideal candidate is a hybrid engineer who understands both large scale data platforms and modern LLM based systems and can translate ideas into practical, reusable solutions.\r\nResponsibilities\r\nBuild and maintain ETL and ELT pipelines using Apache Spark, Hive, and Trino across S3 based data lake environments\r\nDevelop and optimize SQL for large scale datasets including window functions, multi table joins, and complex aggregations\r\nBuild and engineer big data systems using EMR on EC2 and EMR on EKS and develop solutions on analytical platforms such as SageMaker, Domino, and Dataiku\r\nParticipate in data quality monitoring, anomaly detection, and production incident investigation\r\nBuild and productionize LLM powered agents using AWS Bedrock and agent frameworks such as LangChain, LangGraph, AWS Strands or similar\r\nDesign agent harness architectures that combine LLM reasoning with deterministic execution including RAG based SQL generation and structured output validation\r\nImplement agent memory, context management, and tool integration including MCP servers, APIs, and data catalog lookups\r\nBuild evaluation frameworks for agent accuracy including paraphrase robustness, routing precision, and structural consistency\r\nStay informed of advances in LLM frameworks and emerging AI capabilities\r\nSupport proof of concept AI initiatives and scale successful solutions across teams\r\nWrite clean, well tested code and contribute to CI and CD pipelines and infrastructure as code on AWS\r\nEnsure secure handling of sensitive regulatory data with auditable execution traces across both data pipelines and AI outputs\r\nPartner across teams, communicate technical concepts clearly, and maintain documentation\r\nActively learn from senior engineers and contribute to continuous improvement of processes and engineering practices\r\nQualifications\r\nBachelor degree in Computer Science, Data Science, Information Systems or related discipline with at least two years of experience or equivalent work experience\r\nStrong experience building data pipelines using Apache Spark and SQL\r\nExperience with SQL query engines such as Hive and Trino and cloud data platforms including AWS S3, EMR, and Lambda\r\nStrong understanding of large scale data challenges such as data skew, high volume processing, and troubleshooting job failures\r\nHands on experience building LLM powered agent systems that use tools and produce structured outputs\r\nExperience with at least one agent framework such as LangChain, LangGraph, or AWS Strands\r\nKnowledge of prompt engineering, RAG architectures, and context and memory management\r\nExperience working with foundation model APIs such as Anthropic Claude, Amazon Nova, or OpenAI\r\nUnderstanding of agent memory models including working memory, episodic memory, and semantic memory\r\nFamiliarity with agent harness design including guardrails, routing, verification loops, and graceful degradation\r\nHands on experience using AI development tools such as GitHub Copilot, Q Developer, ChatGPT, or Claude\r\nExperience integrating AI into development workflows including code generation, debugging, and testing\r\nStrong experience with AWS services including S3, EMR, Lambda, Bedrock, and Step Functions\r\nExperience using S3 with Spark including file formats and consistency considerations\r\nFamiliarity with AWS monitoring and logging tools such as CloudWatch and CloudTrail\r\nProficiency in Python for data engineering and automation with strong understanding of clean code, modular design, and performance\r\nStrong SQL skills including window functions, joins, aggregations, and handling edge cases such as null values and duplicates\r\nStrong understanding of collections, concurrency, and memory management\r\nExposure to containerization and orchestration technologies such as Docker and Kubernetes\r\nExperience with infrastructure as code and CI and CD pipelines\r\nStrong communication skills and ability to work in a fast paced environment\r\nAbility to quickly learn new technologies and adapt to evolving requirements\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T00:50:16.607Z","dateModified":"2026-06-25T00:50:16.607Z","hiringOrganization":{"@type":"Organization","name":"Tential Solutions","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Vernon","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"01cebb6e191e085ca0171902"},"url":"https://jobsearcher.com/jobs/01cebb6e191e085ca0171902"}}