{"schemaVersion":"jobsearcher.job.v1","id":"e367df91671bb62e93df6ada","url":"https://jobsearcher.com/jobs/e367df91671bb62e93df6ada","canonicalUrl":"https://jobsearcher.com/jobs/e367df91671bb62e93df6ada","title":"Enterprise Data Warehouse Engineer","description":"Job description:\nThe Enterprise Data Warehouse Engineer designs, builds, and operationalizes the data warehouse infrastructure and pipelines that deliver trusted, performant, and governed data for analytics and reporting. Deployed under BlueAngle's managed services model, this engineer serves as the primary technical authority for the data platform on client engagements, working alongside client stakeholders and BlueAngle delivery leadership to ensure data is available, reliable, and fit for downstream consumption.\nThis is a client-facing, delivery-focused role. The successful candidate will translate business requirements into scalable technical architecture, build production-grade pipelines, and transfer knowledge to client and partner teams across the engagement lifecycle.\nLocation: US-based, remote-first.\nTravel Expectation: 0–30%\n2. ROLE OVERVIEW\nThe EDW Engineer is responsible for the full data platform stack — from source system analysis and data modelling through to pipeline development, data quality controls, and performance optimisation. The engineer works within the client environment under the governance framework defined in the BlueAngle Statement of Work, delivering against agreed milestones while maintaining professional standards of documentation and knowledge transfer.\n3. KEY RESPONSIBILITIES\n3.1 Data Architecture & Modelling\nLead source system analysis and data profiling across all in-scope systems.\nDesign the target data model — dimensional (star/snowflake), data vault, or medallion architecture — appropriate to client's requirements and platform.\nDefine schema standards, naming conventions, and conformed dimension frameworks for downstream BI and analytics consumers.\nProduce logical and physical data model documentation as a formal deliverable (D2).\n3.2 Pipeline Development & Orchestration\nBuild, test, and schedule ingestion pipelines from all identified source systems (D3).\nDevelop the curated/conformed transformation layer per the agreed architecture (D4).\nImplement orchestration using [dbt / Azure Data Factory / Airflow / SSIS / other] — to be confirmed with client environment.\nApply CI/CD practices for pipeline versioning, testing, and deployment where the environment supports it.\n3.3 Data Quality & Governance\nDesign and implement data quality rules, validation checks, and reconciliation logic (D5).\nBuild exception handling and alerting to surface data quality failures to the operations team.\nEstablish data lineage documentation and maintain it throughout the engagement.\nImplement governance controls per policy defined by [Client] — the engineer implements; policy ownership remains with the client.\n3.4 Performance, Optimization & Cost\nProfile and tune queries, partitioning strategies, and indexing for performance against agreed SLAs.\nMonitor platform costs and implement optimisations to stay within agreed budget targets.\nMaintain platform health, patching, and version management in line with client change management processes.\n3.5 Stakeholder Engagement & Knowledge Transfer\nProduce a source and requirements assessment (D1) in collaboration with client SMEs and BlueAngle delivery leadership.\nDeliver technical documentation, runbooks, and lineage maps (D6) as formal handover artefacts at engagement close.\nConduct structured knowledge-transfer sessions with client technical staff and partner teams.\nParticipate in weekly status reporting, milestone gate reviews, and escalation processes per engagement governance.\n4. REQUIRED SKILLS & EXPERIENCE\n8–12 years data engineering experience with at least 4 years on the Azure data stack (Synapse, Data Factory, SQL, Python or Scala).\nStrong SQL and data modelling — dimensional modelling, normalization, and/or data vault.\nFamiliarity with medallion / lakehouse architecture where relevant to the target platform.\nDirect experience modelling ERP data (D365 F&O, AX, or SAP).\nAt least one full-lifecycle ERP implementation where they led the warehouse / reporting layer.\nPerformance tuning, query optimization, partitioning, clustering, and cost management on the target platform.\nUnderstanding of cloud storage patterns (data lake, blob/object storage) and their interaction with the warehouse layer.\nExperience building data quality frameworks — validation rules, reconciliation, exception reporting.\nAbility to translate technical concepts for non-technical client stakeholders.\nStrong written English — formal documentation, runbooks, and client-facing deliverables will be produced in English.\nExperience working in MSP, consulting, or client-embedded delivery contexts is strongly preferred.\n5. PREFERRED CERTIFICATIONS\nCloud data platform certification — e.g. Microsoft DP-203 (Azure Data Engineer Associate), Snowflake SnowPro Core, AWS Data Analytics Specialty, or GCP Professional Data Engineer.\ndbt Fundamentals certification or equivalent.\nMicrosoft Certified: Azure Solutions Architect Expert (advantageous for Fabric/Synapse engagements).\nITIL 4 Foundation (desirable for MSP-context engagements).\nBenefits:\n401(k)\nDental insurance\nHealth insurance\nPaid time off\nProfessional development assistance\nVision insurance\nExperience:\nEnterprise Data Warehouse: 8 years (Required)\n\nWillingness to travel:\n30% (Required)\nWork Location: Remote","company":"Blueangle","rawCompany":"blueangle","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-08-15T13:44:18.788Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"},{"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":"Enterprise Data Warehouse Engineer","description":"Job description:\nThe Enterprise Data Warehouse Engineer designs, builds, and operationalizes the data warehouse infrastructure and pipelines that deliver trusted, performant, and governed data for analytics and reporting. Deployed under BlueAngle's managed services model, this engineer serves as the primary technical authority for the data platform on client engagements, working alongside client stakeholders and BlueAngle delivery leadership to ensure data is available, reliable, and fit for downstream consumption.\nThis is a client-facing, delivery-focused role. The successful candidate will translate business requirements into scalable technical architecture, build production-grade pipelines, and transfer knowledge to client and partner teams across the engagement lifecycle.\nLocation: US-based, remote-first.\nTravel Expectation: 0–30%\n2. ROLE OVERVIEW\nThe EDW Engineer is responsible for the full data platform stack — from source system analysis and data modelling through to pipeline development, data quality controls, and performance optimisation. The engineer works within the client environment under the governance framework defined in the BlueAngle Statement of Work, delivering against agreed milestones while maintaining professional standards of documentation and knowledge transfer.\n3. KEY RESPONSIBILITIES\n3.1 Data Architecture & Modelling\nLead source system analysis and data profiling across all in-scope systems.\nDesign the target data model — dimensional (star/snowflake), data vault, or medallion architecture — appropriate to client's requirements and platform.\nDefine schema standards, naming conventions, and conformed dimension frameworks for downstream BI and analytics consumers.\nProduce logical and physical data model documentation as a formal deliverable (D2).\n3.2 Pipeline Development & Orchestration\nBuild, test, and schedule ingestion pipelines from all identified source systems (D3).\nDevelop the curated/conformed transformation layer per the agreed architecture (D4).\nImplement orchestration using [dbt / Azure Data Factory / Airflow / SSIS / other] — to be confirmed with client environment.\nApply CI/CD practices for pipeline versioning, testing, and deployment where the environment supports it.\n3.3 Data Quality & Governance\nDesign and implement data quality rules, validation checks, and reconciliation logic (D5).\nBuild exception handling and alerting to surface data quality failures to the operations team.\nEstablish data lineage documentation and maintain it throughout the engagement.\nImplement governance controls per policy defined by [Client] — the engineer implements; policy ownership remains with the client.\n3.4 Performance, Optimization & Cost\nProfile and tune queries, partitioning strategies, and indexing for performance against agreed SLAs.\nMonitor platform costs and implement optimisations to stay within agreed budget targets.\nMaintain platform health, patching, and version management in line with client change management processes.\n3.5 Stakeholder Engagement & Knowledge Transfer\nProduce a source and requirements assessment (D1) in collaboration with client SMEs and BlueAngle delivery leadership.\nDeliver technical documentation, runbooks, and lineage maps (D6) as formal handover artefacts at engagement close.\nConduct structured knowledge-transfer sessions with client technical staff and partner teams.\nParticipate in weekly status reporting, milestone gate reviews, and escalation processes per engagement governance.\n4. REQUIRED SKILLS & EXPERIENCE\n8–12 years data engineering experience with at least 4 years on the Azure data stack (Synapse, Data Factory, SQL, Python or Scala).\nStrong SQL and data modelling — dimensional modelling, normalization, and/or data vault.\nFamiliarity with medallion / lakehouse architecture where relevant to the target platform.\nDirect experience modelling ERP data (D365 F&O, AX, or SAP).\nAt least one full-lifecycle ERP implementation where they led the warehouse / reporting layer.\nPerformance tuning, query optimization, partitioning, clustering, and cost management on the target platform.\nUnderstanding of cloud storage patterns (data lake, blob/object storage) and their interaction with the warehouse layer.\nExperience building data quality frameworks — validation rules, reconciliation, exception reporting.\nAbility to translate technical concepts for non-technical client stakeholders.\nStrong written English — formal documentation, runbooks, and client-facing deliverables will be produced in English.\nExperience working in MSP, consulting, or client-embedded delivery contexts is strongly preferred.\n5. PREFERRED CERTIFICATIONS\nCloud data platform certification — e.g. Microsoft DP-203 (Azure Data Engineer Associate), Snowflake SnowPro Core, AWS Data Analytics Specialty, or GCP Professional Data Engineer.\ndbt Fundamentals certification or equivalent.\nMicrosoft Certified: Azure Solutions Architect Expert (advantageous for Fabric/Synapse engagements).\nITIL 4 Foundation (desirable for MSP-context engagements).\nBenefits:\n401(k)\nDental insurance\nHealth insurance\nPaid time off\nProfessional development assistance\nVision insurance\nExperience:\nEnterprise Data Warehouse: 8 years (Required)\n\nWillingness to travel:\n30% (Required)\nWork Location: Remote","datePosted":"2026-08-15T13:44:18.788Z","dateModified":"2026-08-15T13:44:18.788Z","hiringOrganization":{"@type":"Organization","name":"Blueangle","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"e367df91671bb62e93df6ada"},"url":"https://jobsearcher.com/jobs/e367df91671bb62e93df6ada"}}