{"schemaVersion":"jobsearcher.job.v1","id":"80c82ece09e8f1e3ee085dc4","url":"https://jobsearcher.com/jobs/80c82ece09e8f1e3ee085dc4","canonicalUrl":"https://jobsearcher.com/jobs/80c82ece09e8f1e3ee085dc4","title":"Manager Data and Analytics Engineering- (GCP/Snowflake)","description":"The Manager, Data and Analytics Engineering leads a team of engineers responsible for delivering scalable, secure, and high-performing data platforms, pipelines, and analytics solutions across business domains. This role drives end-to-end execution of data initiatives, ensuring high standards of engineering excellence, governance, and delivery discipline.\r\nAs a Team Member leader, the Manager is accountable for building and developing technical talent, fostering a culture of innovation and ownership, and aligning team efforts with enterprise priorities. The ideal candidate combines strong engineering experience with business acumen, stakeholder partnership, and a continuous improvement mindset to accelerate the impact of data and analytics across the organization. This role serves as a key bridge between engineering execution and strategic delivery, guiding the team in building well-governed, high-impact data assets that support cross-functional analytics, decision automation, and AI readiness.\r\nThis position is located in Springfield, MO. Remote work is not an option for this role.\r\nResponsibilities and Duties: Provide hands-on leadership in the design, development, and deployment of enterprise-grade data platforms, batch and streaming pipelines, semantics layer and analytics-enabling services.\r\nEnsure the team follows best practices in data engineering, architecture patterns (e.g., medallion, data mesh), and platform-specific optimization (e.g., Snowflake, BigQuery, dbt, Airflow, Prefect).\r\nGuide implementation of secure, cost-efficient, and reusable data products, frameworks, and interfaces across ingestion, transformation, semantics and delivery layers.\r\nPromote adherence to CI/CD, observability, schema management, and infrastructure-as-code practices for resilient data product deployment.\r\nOwn the successful delivery of data initiatives, balancing technical feasibility, scope, timelines, and stakeholder expectations.\r\nEstablish delivery plans, resource plans, sprint cadences, and engineering KPIs to monitor progress, unblock teams, and ensure predictable outcomes.\r\nCollaborate with product owners, business stakeholders, and program teams to define roadmaps, resource needs, and prioritization of data products and platform enhancements.\r\nServe as the escalation point for engineering blockers, architectural decisions, or trade-off discussions, driving resolution across teams.\r\nEnsure team compliance with enterprise data modeling, documentation, and metadata standards.\r\nStandardize technical documentation practices for data models, transformation logic, and platform operations to promote reuse and transparency.\r\nEmbed lineage, data dictionary, platform metadata integration, and architectural documentation into delivery workflows using tools such as Alation, Collibra, and schema registries.\r\nPartner with governance, compliance, and security teams to integrate policy-as-code frameworks, RBAC, and data governance policies into engineering execution.\r\nDrive implementation of data quality frameworks embedded within orchestration and transformation pipelines.\r\nEstablish SLAs, observability dashboards, and automated validation rules for critical data assets and domain-specific pipelines.\r\nLead root cause analysis and continuous improvement for data quality incidents, latency, pipeline failure, ensuring traceability across ingestion, enrichment, and delivery layers.\r\nCollaborate with technology and business teams to operationalize trusted data practices and ensure alignment on quality definitions and expectations.\r\nContribute to shaping data domain strategy by aligning engineering execution to enterprise priorities and architectural principles.\r\nPartner with product, technology, business and architecture leaders to define roadmaps that advance data maturity, platform scalability, and solution interoperability.\r\nChampion platform evolution initiatives such as self-service enablement, AI/ML readiness, and composable data product design.\r\nProvide input to the enterprise architecture council on patterns, trade-offs, and emerging technologies to guide platform modernization.\r\nBuild trusted relationships with product owners, domain leaders, and business stakeholders enterprise domains such as across marketing, supply chain, customer, and store operations.\r\nPresent project status, technical trade-offs, and platform health to both technical and non-technical audiences with clarity and confidence.\r\nRepresent engineering in business domain forums, roadmap sessions, providing insight into data platform capabilities, gaps, and enhancement opportunities.\r\nOversee the delivery of foundational data assets, curated datasets, and semantic layers to drive business outcomes and analytics adoption.\r\nGuide the team in building unified metrics layers, semantic data models, and analytical datasets aligned with business reporting and decisioning needs.\r\nPartner with data science and ML engineering to ensure the semantic layer and metric stores are consistent, reusable, and extensible for downstream use cases.\r\nOversee integration of business logic into transformation layers to support real-time, self-service, and LLM-driven analytics.\r\nEnable rapid insights through well-structured feature stores, reusable dbt models, and aligned dimensional views.\r\nLead change management efforts during platform migrations, architectural shifts, and new technology onboarding.\r\nDrive cultural adoption of modern practices such as declarative data modeling, data contracts, universal semantic layer, streaming-aware designs, and platform-as-a-product mindset.\r\nStay informed on emerging data technologies to shape future-state capabilities and ensure team readiness for evolution.\r\nDefine and drive team development plans aligned with evolving platform and domain requirements, ensuring skill growth and succession planning.\r\nFoster a culture of engineering excellence, agile delivery, collaboration, and accountability across the team.\r\nLead internal communities of practice, technical workshops, and user groups to drive and disseminate best practices.\r\nSkills - Required: Google Cloud Platform knowledge and experience\r\nStrong experience leading the design, development, and deployment of enterprise-grade data platforms and products (e.g., Snowflake, BigQuery, dbt, Airflow, Informatica, and Kafka/PubSub).\r\nProficient in programming languages such as Python and SQL with ability to review code, enforce engineering standards, and support performance optimization in batch and streaming pipelines.\r\nDemonstrated understanding of data architecture principles, including medallion layering, data mesh design, and streaming architectures to support diverse use cases.\r\nExperience implementing CI/CD for data pipelines, schema management workflows, and observability frameworks (e.g., data quality monitors, alerting dashboards, SLOs/SLAs).\r\nPractical experience managing transformation and orchestration layers to deliver curated data assets, semantic layers, and ML feature stores aligned to consumption patterns.\r\nProficiency in metadata-driven engineering, including integration with Alation, Collibra, or similar tools for lineage, data catalogs, and policy enforcement.\r\nProven ability to collaborate with product owners, business stakeholders, and domain SMEs to prioritize data needs, translate requirements into executable engineering tasks, and ensure business value delivery.\r\nDeep understanding of enterprise business processes across functional areas (e.g., supply chain, customer, store ops, finance) and how data platforms can accelerate decision-making.\r\nAbility to evaluate business cases, clarify problem statements, and manage trade-offs between delivery timelines, data quality, performance, and platform sustainability.\r\nExperience in leading the delivery of trusted, governed data assets that directly impact business reporting, operational workflows, and digital transformation initiatives.\r\nDemonstrated leadership in managing data engineering teams, including capacity planning, performance management, skill development, and career growth.\r\nExperience managing sprint cycles, engineering backlogs, and delivery KPIs in collaboration with cross-functional partners to ensure timely and predictable delivery of data products.\r\nAbility to lead by example, mentor engineers, and enforce technical standards and architecture patterns across the team.\r\nSkilled at facilitating architecture reviews, design sessions, and engineering forums to drive consensus and technical decision-making.\r\nExperience serving as an escalation point for resolving platform blockers, resourcing risks, and architecture trade-offs with engineering and business leaders.\r\nExperience aligning engineering execution with enterprise data strategy, including platform modernization, AI readiness, and semantic unification.\r\nExperience supporting initiatives like universal semantic layer design, KPI harmonization, and enabling consistent, reusable metric definitions across the business.\r\nStrong competence in partnering with architecture, governance, and platform teams to ensure delivery aligns to broader enterprise blueprints, privacy policies, and compliance frameworks.\r\nAbility to promote platform improvements and automation opportunities (e.g., lineage, schema evolution, versioned metadata, self-service enablement).\r\nSkills - Preferred: Experience implementing or governing open table formats (e.g., Iceberg) and cloud-agnostic data exchange architectures for hybrid, multi-cloud scale and interoperability.\r\nFamiliarity with dbt modular modeling, metrics layer design, and streaming-aware semantic patterns that support real-time insights and ML model training pipelines.\r\nProven track record leading or contributing to cloud migration initiatives, architectural modernization, or platform consolidation programs in enterprise environments.\r\nExposure to policy-as-code, RBAC frameworks, and data security automation integrated into engineering workflows.\r\nAbility to drive change management and cultural adoption of engineering practices such as data contracts, declarative modeling, and streaming delivery.\r\nExperience contributing to technical documentation standards, onboarding playbooks, and internal engineering communities of practice.\r\nO'Reilly Auto Parts has a proven track record of growth and stability. O'Reilly is full of successful career stories and believes in a strong promote-from-within philosophy, encouraging you to grow your career along with the organization.\r\nTotal Compensation Package: Competitive Wages & Paid Time Off\r\nStock Purchase Plan & 401k with Employer Contributions Starting Day One\r\nMedical, Dental & Vision Insurance with Optional Flexible Spending Account (FSA)\r\nTeam Member Health/Wellbeing Programs\r\nTuition Educational Assistance Programs\r\nOpportunities for Career Growth\r\nO'Reilly Auto Parts is an equal opportunity employer. The Company does not discriminate on the basis of race, religion, color, national origin or ancestry (including immigration status or citizenship), sex, sexual orientation, gender identity, pregnancy (including childbirth, lactation, and related medical conditions), age (40 and over), veteran status, uniformed service member status, physical or mental disability, genetic information (including testing or characteristics) or another protected status as defined by local, state, or federal law, as applicable. Qualified individuals with a disability may be entitled to reasonable accommodation under the Americans with Disabilities Act. If you require a reasonable accommodation during the application or employment process, please send an email to rar@oreillyauto.com or call (800) 471-7431 option, and provide your requested accommodation, and position details.\r\nJ-18808-Ljbffr","company":"Swiftcruit","rawCompany":"swiftcruit","city":"Pierce","state":"ID","isRemote":false,"isActive":false,"createdAt":"2026-07-25T00:28:21.279Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"11-3021.00","title":"Computer and Information Systems Managers","slug":"computer-and-information-systems-managers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Manager Data and Analytics Engineering- (GCP/Snowflake)","description":"The Manager, Data and Analytics Engineering leads a team of engineers responsible for delivering scalable, secure, and high-performing data platforms, pipelines, and analytics solutions across business domains. This role drives end-to-end execution of data initiatives, ensuring high standards of engineering excellence, governance, and delivery discipline.\r\nAs a Team Member leader, the Manager is accountable for building and developing technical talent, fostering a culture of innovation and ownership, and aligning team efforts with enterprise priorities. The ideal candidate combines strong engineering experience with business acumen, stakeholder partnership, and a continuous improvement mindset to accelerate the impact of data and analytics across the organization. This role serves as a key bridge between engineering execution and strategic delivery, guiding the team in building well-governed, high-impact data assets that support cross-functional analytics, decision automation, and AI readiness.\r\nThis position is located in Springfield, MO. Remote work is not an option for this role.\r\nResponsibilities and Duties: Provide hands-on leadership in the design, development, and deployment of enterprise-grade data platforms, batch and streaming pipelines, semantics layer and analytics-enabling services.\r\nEnsure the team follows best practices in data engineering, architecture patterns (e.g., medallion, data mesh), and platform-specific optimization (e.g., Snowflake, BigQuery, dbt, Airflow, Prefect).\r\nGuide implementation of secure, cost-efficient, and reusable data products, frameworks, and interfaces across ingestion, transformation, semantics and delivery layers.\r\nPromote adherence to CI/CD, observability, schema management, and infrastructure-as-code practices for resilient data product deployment.\r\nOwn the successful delivery of data initiatives, balancing technical feasibility, scope, timelines, and stakeholder expectations.\r\nEstablish delivery plans, resource plans, sprint cadences, and engineering KPIs to monitor progress, unblock teams, and ensure predictable outcomes.\r\nCollaborate with product owners, business stakeholders, and program teams to define roadmaps, resource needs, and prioritization of data products and platform enhancements.\r\nServe as the escalation point for engineering blockers, architectural decisions, or trade-off discussions, driving resolution across teams.\r\nEnsure team compliance with enterprise data modeling, documentation, and metadata standards.\r\nStandardize technical documentation practices for data models, transformation logic, and platform operations to promote reuse and transparency.\r\nEmbed lineage, data dictionary, platform metadata integration, and architectural documentation into delivery workflows using tools such as Alation, Collibra, and schema registries.\r\nPartner with governance, compliance, and security teams to integrate policy-as-code frameworks, RBAC, and data governance policies into engineering execution.\r\nDrive implementation of data quality frameworks embedded within orchestration and transformation pipelines.\r\nEstablish SLAs, observability dashboards, and automated validation rules for critical data assets and domain-specific pipelines.\r\nLead root cause analysis and continuous improvement for data quality incidents, latency, pipeline failure, ensuring traceability across ingestion, enrichment, and delivery layers.\r\nCollaborate with technology and business teams to operationalize trusted data practices and ensure alignment on quality definitions and expectations.\r\nContribute to shaping data domain strategy by aligning engineering execution to enterprise priorities and architectural principles.\r\nPartner with product, technology, business and architecture leaders to define roadmaps that advance data maturity, platform scalability, and solution interoperability.\r\nChampion platform evolution initiatives such as self-service enablement, AI/ML readiness, and composable data product design.\r\nProvide input to the enterprise architecture council on patterns, trade-offs, and emerging technologies to guide platform modernization.\r\nBuild trusted relationships with product owners, domain leaders, and business stakeholders enterprise domains such as across marketing, supply chain, customer, and store operations.\r\nPresent project status, technical trade-offs, and platform health to both technical and non-technical audiences with clarity and confidence.\r\nRepresent engineering in business domain forums, roadmap sessions, providing insight into data platform capabilities, gaps, and enhancement opportunities.\r\nOversee the delivery of foundational data assets, curated datasets, and semantic layers to drive business outcomes and analytics adoption.\r\nGuide the team in building unified metrics layers, semantic data models, and analytical datasets aligned with business reporting and decisioning needs.\r\nPartner with data science and ML engineering to ensure the semantic layer and metric stores are consistent, reusable, and extensible for downstream use cases.\r\nOversee integration of business logic into transformation layers to support real-time, self-service, and LLM-driven analytics.\r\nEnable rapid insights through well-structured feature stores, reusable dbt models, and aligned dimensional views.\r\nLead change management efforts during platform migrations, architectural shifts, and new technology onboarding.\r\nDrive cultural adoption of modern practices such as declarative data modeling, data contracts, universal semantic layer, streaming-aware designs, and platform-as-a-product mindset.\r\nStay informed on emerging data technologies to shape future-state capabilities and ensure team readiness for evolution.\r\nDefine and drive team development plans aligned with evolving platform and domain requirements, ensuring skill growth and succession planning.\r\nFoster a culture of engineering excellence, agile delivery, collaboration, and accountability across the team.\r\nLead internal communities of practice, technical workshops, and user groups to drive and disseminate best practices.\r\nSkills - Required: Google Cloud Platform knowledge and experience\r\nStrong experience leading the design, development, and deployment of enterprise-grade data platforms and products (e.g., Snowflake, BigQuery, dbt, Airflow, Informatica, and Kafka/PubSub).\r\nProficient in programming languages such as Python and SQL with ability to review code, enforce engineering standards, and support performance optimization in batch and streaming pipelines.\r\nDemonstrated understanding of data architecture principles, including medallion layering, data mesh design, and streaming architectures to support diverse use cases.\r\nExperience implementing CI/CD for data pipelines, schema management workflows, and observability frameworks (e.g., data quality monitors, alerting dashboards, SLOs/SLAs).\r\nPractical experience managing transformation and orchestration layers to deliver curated data assets, semantic layers, and ML feature stores aligned to consumption patterns.\r\nProficiency in metadata-driven engineering, including integration with Alation, Collibra, or similar tools for lineage, data catalogs, and policy enforcement.\r\nProven ability to collaborate with product owners, business stakeholders, and domain SMEs to prioritize data needs, translate requirements into executable engineering tasks, and ensure business value delivery.\r\nDeep understanding of enterprise business processes across functional areas (e.g., supply chain, customer, store ops, finance) and how data platforms can accelerate decision-making.\r\nAbility to evaluate business cases, clarify problem statements, and manage trade-offs between delivery timelines, data quality, performance, and platform sustainability.\r\nExperience in leading the delivery of trusted, governed data assets that directly impact business reporting, operational workflows, and digital transformation initiatives.\r\nDemonstrated leadership in managing data engineering teams, including capacity planning, performance management, skill development, and career growth.\r\nExperience managing sprint cycles, engineering backlogs, and delivery KPIs in collaboration with cross-functional partners to ensure timely and predictable delivery of data products.\r\nAbility to lead by example, mentor engineers, and enforce technical standards and architecture patterns across the team.\r\nSkilled at facilitating architecture reviews, design sessions, and engineering forums to drive consensus and technical decision-making.\r\nExperience serving as an escalation point for resolving platform blockers, resourcing risks, and architecture trade-offs with engineering and business leaders.\r\nExperience aligning engineering execution with enterprise data strategy, including platform modernization, AI readiness, and semantic unification.\r\nExperience supporting initiatives like universal semantic layer design, KPI harmonization, and enabling consistent, reusable metric definitions across the business.\r\nStrong competence in partnering with architecture, governance, and platform teams to ensure delivery aligns to broader enterprise blueprints, privacy policies, and compliance frameworks.\r\nAbility to promote platform improvements and automation opportunities (e.g., lineage, schema evolution, versioned metadata, self-service enablement).\r\nSkills - Preferred: Experience implementing or governing open table formats (e.g., Iceberg) and cloud-agnostic data exchange architectures for hybrid, multi-cloud scale and interoperability.\r\nFamiliarity with dbt modular modeling, metrics layer design, and streaming-aware semantic patterns that support real-time insights and ML model training pipelines.\r\nProven track record leading or contributing to cloud migration initiatives, architectural modernization, or platform consolidation programs in enterprise environments.\r\nExposure to policy-as-code, RBAC frameworks, and data security automation integrated into engineering workflows.\r\nAbility to drive change management and cultural adoption of engineering practices such as data contracts, declarative modeling, and streaming delivery.\r\nExperience contributing to technical documentation standards, onboarding playbooks, and internal engineering communities of practice.\r\nO'Reilly Auto Parts has a proven track record of growth and stability. O'Reilly is full of successful career stories and believes in a strong promote-from-within philosophy, encouraging you to grow your career along with the organization.\r\nTotal Compensation Package: Competitive Wages & Paid Time Off\r\nStock Purchase Plan & 401k with Employer Contributions Starting Day One\r\nMedical, Dental & Vision Insurance with Optional Flexible Spending Account (FSA)\r\nTeam Member Health/Wellbeing Programs\r\nTuition Educational Assistance Programs\r\nOpportunities for Career Growth\r\nO'Reilly Auto Parts is an equal opportunity employer. The Company does not discriminate on the basis of race, religion, color, national origin or ancestry (including immigration status or citizenship), sex, sexual orientation, gender identity, pregnancy (including childbirth, lactation, and related medical conditions), age (40 and over), veteran status, uniformed service member status, physical or mental disability, genetic information (including testing or characteristics) or another protected status as defined by local, state, or federal law, as applicable. Qualified individuals with a disability may be entitled to reasonable accommodation under the Americans with Disabilities Act. If you require a reasonable accommodation during the application or employment process, please send an email to rar@oreillyauto.com or call (800) 471-7431 option, and provide your requested accommodation, and position details.\r\nJ-18808-Ljbffr","datePosted":"2026-07-25T00:28:21.279Z","dateModified":"2026-07-25T00:28:21.279Z","hiringOrganization":{"@type":"Organization","name":"Swiftcruit","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Pierce","addressRegion":"ID","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"80c82ece09e8f1e3ee085dc4"},"url":"https://jobsearcher.com/jobs/80c82ece09e8f1e3ee085dc4"}}