{"schemaVersion":"jobsearcher.job.v1","id":"0b88e9aeb4bcdf63eb2c6ccc","url":"https://jobsearcher.com/jobs/0b88e9aeb4bcdf63eb2c6ccc","canonicalUrl":"https://jobsearcher.com/jobs/0b88e9aeb4bcdf63eb2c6ccc","title":"IT Principal Data Engineering","description":"PurposeWe are looking for a Principal Data Engineer to sit at the intersection of data engineering and applied data science. You will own the design, development, and operation of the platforms and pipelines that power our data science capabilities - ensuring data flows reliably from source systems through to analysis, and business consumption.This role is roughly 75% data engineering and 25% data science and is ideal for someone who builds with engineering rigor but thinks with a data science mindset; someone who is energized by building platforms that make AI real in an organization. The right candidate is curious by nature - you explore out-of-the-box ideas and stay current with the fast-moving AI/Machine Learning (ML) landscape.You'll work directly with business analysts, product owners, business end-users, engineering and application teams, and our own data/platform engineering teams. A consultative communication style is critical as shared outcomes across technology and business are the expectation.ResponsibilitiesPlatform Architecture & StrategyDefine the long-term technical direction for the data science platform and integration with existing ELT pipelinesEnsure platforms are scalable, reliable, secure, and cost-efficient at enterprise scaleEvaluate and adopt emerging tools in the modern data and ML stackData Engineering DevelopmentDesign, develop, and optimize ETL pipelines and outbound data feedsDevelop and follow templates and engineering patterns to reduce the time-to-deploy new data assets or changes to an existing data model or analytics solutionsPartner with key business teams to understand their data needs and assist them in building appropriate data solutions to meet their business needsData Science DevelopmentDesign, build, and optimize end-to-end data science pipelines - from raw data ingestion through feature engineering, model training, and inference servingContribute to MLOps practices including model versioning and monitoring, supporting the transition of data science work into productionTechnical Leadership & MentorshipProvide technical guidance to data engineersConduct code reviews and champion engineering best practices across workstreamsLead without direct authority, influencing cross-functional teams across data engineering, analytics and product ownersData Governance & QualityEstablish best practices for data quality, lineage, privacy, and security across data engineering and science pipelinesEnsure model inputs and outputs are auditable, reproducible, and compliant with data governance standardsStakeholder ManagementPartner with data engineering, product owners, and software engineers to align platform capabilities with organizational AI/ML goalsTranslate complex technical concepts into clear, actionable insights for non-technical stakeholdersAbout YouBachelor's degree in computer science, engineering, mathematics, or a related field, OR 7+ years of equivalent verifiable experience, skillset, and record of accomplishmentExperience in a Principal or Senior Data Engineer role with direct involvement in ML platform or Data Science workProficiency in an analytics/BI tool such as Power BIData Engineering experience:Modern data stack technologies - Databricks (strongly preferred), Snowflake, SparkInbound/outbound transportation of data with APIs and FTPsMPP databases such as Databricks, Snowflake, BigQuery, Teradata, or Azure SynapseCloud platforms - AWS, Azure, or GCPPython and SQLML & Data Science experienceBuilding and deploying ML models (classification, regression, forecasting, NLP, or similar)Familiarity with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlowMLflow or similar tools for experiment tracking, model registry, and deploymentUnderstanding of feature engineering, model evaluation, and common ML failure modesArchitecture experienceStrong understanding of data modelling techniques (Kimball, Data Vault) and distributed systemsFamiliarity with feature stores, training pipelines, and batch/real-time inference architectures","company":"Savealot","rawCompany":"savealot","city":"St Ann","state":"MO","isRemote":false,"isActive":false,"createdAt":"2026-06-18T04:08:20.740Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-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":"IT Principal Data Engineering","description":"PurposeWe are looking for a Principal Data Engineer to sit at the intersection of data engineering and applied data science. You will own the design, development, and operation of the platforms and pipelines that power our data science capabilities - ensuring data flows reliably from source systems through to analysis, and business consumption.This role is roughly 75% data engineering and 25% data science and is ideal for someone who builds with engineering rigor but thinks with a data science mindset; someone who is energized by building platforms that make AI real in an organization. The right candidate is curious by nature - you explore out-of-the-box ideas and stay current with the fast-moving AI/Machine Learning (ML) landscape.You'll work directly with business analysts, product owners, business end-users, engineering and application teams, and our own data/platform engineering teams. A consultative communication style is critical as shared outcomes across technology and business are the expectation.ResponsibilitiesPlatform Architecture & StrategyDefine the long-term technical direction for the data science platform and integration with existing ELT pipelinesEnsure platforms are scalable, reliable, secure, and cost-efficient at enterprise scaleEvaluate and adopt emerging tools in the modern data and ML stackData Engineering DevelopmentDesign, develop, and optimize ETL pipelines and outbound data feedsDevelop and follow templates and engineering patterns to reduce the time-to-deploy new data assets or changes to an existing data model or analytics solutionsPartner with key business teams to understand their data needs and assist them in building appropriate data solutions to meet their business needsData Science DevelopmentDesign, build, and optimize end-to-end data science pipelines - from raw data ingestion through feature engineering, model training, and inference servingContribute to MLOps practices including model versioning and monitoring, supporting the transition of data science work into productionTechnical Leadership & MentorshipProvide technical guidance to data engineersConduct code reviews and champion engineering best practices across workstreamsLead without direct authority, influencing cross-functional teams across data engineering, analytics and product ownersData Governance & QualityEstablish best practices for data quality, lineage, privacy, and security across data engineering and science pipelinesEnsure model inputs and outputs are auditable, reproducible, and compliant with data governance standardsStakeholder ManagementPartner with data engineering, product owners, and software engineers to align platform capabilities with organizational AI/ML goalsTranslate complex technical concepts into clear, actionable insights for non-technical stakeholdersAbout YouBachelor's degree in computer science, engineering, mathematics, or a related field, OR 7+ years of equivalent verifiable experience, skillset, and record of accomplishmentExperience in a Principal or Senior Data Engineer role with direct involvement in ML platform or Data Science workProficiency in an analytics/BI tool such as Power BIData Engineering experience:Modern data stack technologies - Databricks (strongly preferred), Snowflake, SparkInbound/outbound transportation of data with APIs and FTPsMPP databases such as Databricks, Snowflake, BigQuery, Teradata, or Azure SynapseCloud platforms - AWS, Azure, or GCPPython and SQLML & Data Science experienceBuilding and deploying ML models (classification, regression, forecasting, NLP, or similar)Familiarity with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlowMLflow or similar tools for experiment tracking, model registry, and deploymentUnderstanding of feature engineering, model evaluation, and common ML failure modesArchitecture experienceStrong understanding of data modelling techniques (Kimball, Data Vault) and distributed systemsFamiliarity with feature stores, training pipelines, and batch/real-time inference architectures","datePosted":"2026-06-18T04:08:20.740Z","dateModified":"2026-06-18T04:08:20.740Z","hiringOrganization":{"@type":"Organization","name":"Savealot","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"St Ann","addressRegion":"MO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0b88e9aeb4bcdf63eb2c6ccc"},"url":"https://jobsearcher.com/jobs/0b88e9aeb4bcdf63eb2c6ccc"}}