{"schemaVersion":"jobsearcher.job.v1","id":"78f43756ff83348fb3aacfb0","url":"https://jobsearcher.com/jobs/78f43756ff83348fb3aacfb0","canonicalUrl":"https://jobsearcher.com/jobs/78f43756ff83348fb3aacfb0","title":"Senior Data Engineer","description":"About the Role\r\nThe data engineer role is changing. The traditional pattern—build a pipeline, hand it off, wait for someone else to figure out if the data is right—doesn't work anymore. The engineers who create the most value now are the ones who go deep into the business domain, understand the problem firsthand, and use AI tools to move from question to working solution in hours instead of sprints.\r\nMDVIP's Analytics team is looking for a Senior Data Engineer who operates as a hybrid technical product owner: someone who builds and maintains the data platform on Azure Databricks, but who also sits with business stakeholders, interrogates the problem, and owns the outcome end-to-end. You won't wait for requirements to be handed to you. You'll go find them, validate them, and ship the solution—using Claude Code and agentic development patterns to collapse the distance between understanding a business problem and solving it in production.\r\nThis is what it means to shift the engineer left into the business domain. You're not a pipeline builder waiting for a ticket. You're the person who understands how physician network growth, member engagement, and operational performance actually work—and who builds the data infrastructure that makes the entire Analytics team faster, sharper, and more impactful.\r\nWhat You'll Do\r\nOwn the Data Platform\r\nDesign, build, and operate MDVIP's data platform on Azure Databricks—ingestion, transformation, storage, and serving layers that power analytics, AI models, and operational reporting.\r\nBuild and maintain data pipelines across MDVIP's ecosystem: Salesforce, SQL Server, Snowflake, third-party sources, and the new cloud-native payments platform.\r\nEngineer for quality and trust—validation checks, anomaly detection, lineage tracking, and documentation that ensure every downstream consumer can rely on the data.\r\nWrite clean, version-controlled, production-grade code. Think like a software engineer building a product, not a script runner maintaining jobs.\r\nGo Deep Into the Business Domain\r\nPartner directly with business stakeholders across physician growth, member services, finance, and operations to understand how data drives decisions—then build for those decisions, not for abstract requirements.\r\nAct as a technical product owner for your domain areas: own the backlog, prioritize based on business impact, and ship iteratively without waiting for a PM to sequence your work.\r\nTranslate ambiguous business questions into data models, feature tables, and curated datasets that analysts and data scientists can build on immediately.\r\nClose the loop—follow your data through to the dashboard, the model, or the operational workflow and validate that it's actually driving the outcome.\r\nDrive AI-First Engineering Practices\r\nUse Claude Code and agentic development as your primary workflow—AI-driven pipeline generation, automated testing, rapid prototyping—to ship at a pace that would be impossible with traditional approaches.\r\nBuild data infrastructure that is AI-ready: well-documented, semantically clear, and structured so that AI tools and agents can reason over it effectively.\r\nScout, evaluate, and adopt emerging AI tools and platforms that make the data team faster—separating real value from hype with hands-on testing.\r\nShare what you learn. Document patterns, run demos, and help the broader team adopt AI-first workflows with confidence.\r\nWho You Are\r\nA data engineer who refuses to stay in the technical silo. You go find the business problem, not wait for it to arrive as a Jira ticket.\r\nSomeone who thinks like a product owner—you prioritize by impact, ship incrementally, and own the outcome, not just the pipeline.\r\nThe kind of engineer who's already using AI tools to write, test, and deploy code before anyone asked you to—and who knows when the output needs a human eye.\r\nEqually comfortable writing a Spark transformation, debugging a Salesforce data sync, presenting findings to leadership, and pushing back on a vague requirement until it's sharp enough to build against.\r\nPragmatic over perfectionist. You optimize for business impact and speed to value, not theoretical elegance.\r\nA strong collaborator who elevates the people around them through clear communication, reusable patterns, and generous knowledge sharing.\r\nRequired Qualifications\r\nBS in Computer Science, Data Science, or related field; 6+ years in data engineering or a hybrid data engineering/analytics role.\r\nDeep hands-on experience with Azure Databricks—notebooks, Delta Lake, Unity Catalog, and production-scale pipelines.\r\nStrong Python and SQL; experience with PySpark and distributed data processing.\r\nBuilt and operated data pipelines that serve analytics, ML models, and operational systems—not just batch ETL jobs.\r\nWorked directly with business stakeholders to define requirements, shape data products, and deliver measurable outcomes.\r\nActive, daily use of AI coding tools (Claude Code, Copilot, or similar) as a force multiplier.\r\nStrong communication skills with a track record of presenting technical work to non-technical audiences.\r\nJ-18808-Ljbffr","company":"Totalperform","rawCompany":"totalperform","city":"Maple","state":"WI","isRemote":false,"isActive":false,"createdAt":"2026-06-25T01:21:51.538Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"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":"Senior Data Engineer","description":"About the Role\r\nThe data engineer role is changing. The traditional pattern—build a pipeline, hand it off, wait for someone else to figure out if the data is right—doesn't work anymore. The engineers who create the most value now are the ones who go deep into the business domain, understand the problem firsthand, and use AI tools to move from question to working solution in hours instead of sprints.\r\nMDVIP's Analytics team is looking for a Senior Data Engineer who operates as a hybrid technical product owner: someone who builds and maintains the data platform on Azure Databricks, but who also sits with business stakeholders, interrogates the problem, and owns the outcome end-to-end. You won't wait for requirements to be handed to you. You'll go find them, validate them, and ship the solution—using Claude Code and agentic development patterns to collapse the distance between understanding a business problem and solving it in production.\r\nThis is what it means to shift the engineer left into the business domain. You're not a pipeline builder waiting for a ticket. You're the person who understands how physician network growth, member engagement, and operational performance actually work—and who builds the data infrastructure that makes the entire Analytics team faster, sharper, and more impactful.\r\nWhat You'll Do\r\nOwn the Data Platform\r\nDesign, build, and operate MDVIP's data platform on Azure Databricks—ingestion, transformation, storage, and serving layers that power analytics, AI models, and operational reporting.\r\nBuild and maintain data pipelines across MDVIP's ecosystem: Salesforce, SQL Server, Snowflake, third-party sources, and the new cloud-native payments platform.\r\nEngineer for quality and trust—validation checks, anomaly detection, lineage tracking, and documentation that ensure every downstream consumer can rely on the data.\r\nWrite clean, version-controlled, production-grade code. Think like a software engineer building a product, not a script runner maintaining jobs.\r\nGo Deep Into the Business Domain\r\nPartner directly with business stakeholders across physician growth, member services, finance, and operations to understand how data drives decisions—then build for those decisions, not for abstract requirements.\r\nAct as a technical product owner for your domain areas: own the backlog, prioritize based on business impact, and ship iteratively without waiting for a PM to sequence your work.\r\nTranslate ambiguous business questions into data models, feature tables, and curated datasets that analysts and data scientists can build on immediately.\r\nClose the loop—follow your data through to the dashboard, the model, or the operational workflow and validate that it's actually driving the outcome.\r\nDrive AI-First Engineering Practices\r\nUse Claude Code and agentic development as your primary workflow—AI-driven pipeline generation, automated testing, rapid prototyping—to ship at a pace that would be impossible with traditional approaches.\r\nBuild data infrastructure that is AI-ready: well-documented, semantically clear, and structured so that AI tools and agents can reason over it effectively.\r\nScout, evaluate, and adopt emerging AI tools and platforms that make the data team faster—separating real value from hype with hands-on testing.\r\nShare what you learn. Document patterns, run demos, and help the broader team adopt AI-first workflows with confidence.\r\nWho You Are\r\nA data engineer who refuses to stay in the technical silo. You go find the business problem, not wait for it to arrive as a Jira ticket.\r\nSomeone who thinks like a product owner—you prioritize by impact, ship incrementally, and own the outcome, not just the pipeline.\r\nThe kind of engineer who's already using AI tools to write, test, and deploy code before anyone asked you to—and who knows when the output needs a human eye.\r\nEqually comfortable writing a Spark transformation, debugging a Salesforce data sync, presenting findings to leadership, and pushing back on a vague requirement until it's sharp enough to build against.\r\nPragmatic over perfectionist. You optimize for business impact and speed to value, not theoretical elegance.\r\nA strong collaborator who elevates the people around them through clear communication, reusable patterns, and generous knowledge sharing.\r\nRequired Qualifications\r\nBS in Computer Science, Data Science, or related field; 6+ years in data engineering or a hybrid data engineering/analytics role.\r\nDeep hands-on experience with Azure Databricks—notebooks, Delta Lake, Unity Catalog, and production-scale pipelines.\r\nStrong Python and SQL; experience with PySpark and distributed data processing.\r\nBuilt and operated data pipelines that serve analytics, ML models, and operational systems—not just batch ETL jobs.\r\nWorked directly with business stakeholders to define requirements, shape data products, and deliver measurable outcomes.\r\nActive, daily use of AI coding tools (Claude Code, Copilot, or similar) as a force multiplier.\r\nStrong communication skills with a track record of presenting technical work to non-technical audiences.\r\nJ-18808-Ljbffr","datePosted":"2026-06-25T01:21:51.538Z","dateModified":"2026-06-25T01:21:51.538Z","hiringOrganization":{"@type":"Organization","name":"Totalperform","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Maple","addressRegion":"WI","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"78f43756ff83348fb3aacfb0"},"url":"https://jobsearcher.com/jobs/78f43756ff83348fb3aacfb0"}}