{"schemaVersion":"jobsearcher.job.v1","id":"409c704598e6f3a41d85cfd2","url":"https://jobsearcher.com/jobs/409c704598e6f3a41d85cfd2","canonicalUrl":"https://jobsearcher.com/jobs/409c704598e6f3a41d85cfd2","title":"Analytics Engineer","description":"Description:\n\nAbout MLG Capital\nMLG Capital is a private real estate investment manager focused on delivering long-term, tax-efficient, risk-adjusted returns through diversified real estate strategies across the United States. As our platform continues to scale nationally, we are strengthening our data and reporting capabilities to drive operational efficiency, improve decision-making, and reduce the time required to produce core business insights.\nThis is MLG’s first-ever Analytics Engineer hire, supporting the modernization of reporting, analytics, and data operations across the firm.\nRole Overview\nThis role is designed as a modern hybrid data position that sits between traditional analytics, BI development, and engineering. Rather than hiring a narrowly scoped reporting analyst or narrow data engineer, MLG Capital is adding a versatile utility player who can help move data from source to insight, supporting backend data operations, shaping analytics-ready models, and enabling high-value dashboards, reporting, and business intelligence across the firm.\nThe person in this role will partner closely with the existing BI Developer and Data Engineer to increase team throughput, improve scalability, and strengthen the foundation for current BI reporting, future AI & predictive analytics, and enterprise data initiatives. This is a strong opportunity for a high-upside mid-level experienced candidate who wants broader ownership, cross-functional exposure, and a clear path to grow with a scaling enterprise data organization.\nHow This Role Differs from a Traditional Data Analyst\n\nThis role is broader and more foundational than a traditional Data Analyst position. A traditional analyst often focuses primarily on report production, ad hoc analysis, dashboard consumption, and answering business questions using already-prepared data. This role goes further upstream and downstream: helping shape the data models, supporting pipeline and platform operations, improving data reliability, and building reusable analytics assets that make the entire organization more scalable. It bridges analytics, engineering, and business enablement by combining technical execution with stakeholder-facing problem-solving.\nKey Responsibilities\nBuild, maintain, and improve analytics-ready datasets, transformations, and data models that support reporting, dashboarding, and downstream analysis.\nSupport data pipelines, ETL/ELT workflows, and automation processes across the Microsoft ecosystem, including Azure Data Factory, Azure Functions, Azure, VS Code and others Microsoft workflows.\nPartner with the BI Developer to develop, enhance, and maintain Power BI dashboards, semantic models, recurring reporting, data export functions and self-service analytics assets.\nWork alongside the Data Engineer to troubleshoot data issues, improve data reliability, monitor pipeline health, and help scale core enterprise data architecture.\nPartner with the BI Lead to translate business requirements into clear technical requirements, data definitions, and implementation plans.\nExecute on approved requirements by building, testing, and refining data models, reporting solutions, and supporting workflows in partnership with the BI Lead.\nContribute to data quality, governance, lineage, and documentation efforts that improve trust, auditability, and long-term maintainability of enterprise data assets.\nSupport the evolution of MLG’s modern data platform, including cloud architecture, data organization standards, and scalable analytics practices.\nIdentify opportunities to reduce manual work, improve throughput, and create reusable data products that accelerate business insight delivery.\nHelp prepare the organization for more advanced analytics use cases by strengthening foundational data structures for AI, predictive analytics, and intelligent automation.\nServe as a flexible, team-oriented utility player who can shift across analytics engineering, BI support, stakeholder problem-solving, and platform operations as priorities evolve.\nIdentify gaps in data feeds and scope new sources to advance analytics capabilities.\nRecommend and implement enhancements that support the firm's strategic goals for data-driven decision-making.\nRequirements:\n\nRequired Qualifications\n3–6 years of experience in a data, BI, analytics, or analytics engineering role.\nStrong SQL skills and experience working with structured datasets, transformations, joins, and performance-conscious query design.\nHands-on experience with Power BI, including dashboard/report development and data modeling concepts.\nWorking knowledge of cloud data platforms and modern data workflows, preferably within Microsoft Azure and/or Microsoft Data Factory.\nExperience in ETL/ELT processes, data pipelines, orchestration tools, or backend data operations.\nUnderstanding of dimensional modeling, semantic modeling, and analytics engineering concepts.\nAbility to work across technical and business teams, gather requirements, define metrics, and communicate clearly with stakeholders.\nStrong problem-solving skills, intellectual curiosity, and a practical mindset for improving processes and scaling data capabilities.\n\nPreferred Qualifications\nExperience with Azure Data Factory, Azure Functions, Microsoft Fabric, Microsoft Purview, or similar modern cloud data tools.\nExperience supporting enterprise data architecture, data governance, metadata, lineage, or data quality frameworks.\nExposure to Python, automation scripting, or API-based integrations.\nExperience in financial services, real estate, asset management, or other regulated data environments.\nFamiliarity with Microsoft AI stack a plus (Foundry, CoPilot Studio, Powerautomate, PowerApps)\nFamiliarity with predictive analytics or data preparation needs for AI use cases.\nExperience working in a growing organization where adaptability, prioritization, and cross-functional collaboration are essential.\nWhat Success Looks Like in the First 12 Months\n\nWithin the first 12 months, this person is successfully contributing across the data lifecycle rather than operating in a narrow lane. They have improved or helped maintain key Data Factory pipelines and datasets, made meaningful contributions to Power BI reporting and semantic models, reduced friction or manual effort in at least a few recurring workflows, and become a trusted cross-functional partner to business stakeholders. They are helping the team move faster, with stronger data quality, clearer definitions, and better operational reliability. Just as importantly, they are helping build the data foundation needed for future AI, predictive analytics, and enterprise-scale decision support.\n\nAdditional Notes:\nPhysical Requirements: Ability to operate office machinery; including but not limited to: telephone, computer, copy machine, fax machine, printer, and mobile phone. Ability to sit for extended periods (up to 4 hours) and use a computer for up to 8 hours per day. Ability to lift up to 10 pounds on an occasional basis.\nWorking Conditions: Open office workstation environment, quiet to moderate noise levels.\nSEC Compliance: As MLG has a subsidiary Registered Investment Adviser, many employees are subject to SEC-mandated compliance requirements. As part of these requirements, employees must disclose personal brokerage accounts and financial holdings, for themselves and any household members whose investment activities they influence.\nThis information is collected solely for regulatory compliance and conflict of interest monitoring. All disclosures are handled with strict confidentiality and are accessible only to the Chief Compliance Officer and designated compliance personnel when a business or SEC related need arises\nThis description is not intended to be all-inclusive; the employee may perform other duties as required.\nAll qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, disability, sexual orientation, national origin or any other category protected by law.\nIn compliance with the Americans with Disabilities Act, a “reasonable accommodation” will be made for an individual with a known physical or mental limitation unless it would require an action of significant difficult causing undue hardship.","company":"Mlgcapital","rawCompany":"mlgcapital","city":"Brookfield","state":"WI","isRemote":false,"isActive":false,"createdAt":"2026-07-23T15:37:34.528Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-2051.01","title":"Business Intelligence Analysts","slug":"business-intelligence-analysts"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"523940","title":"Portfolio Management and Investment Advice","slug":"portfolio-management-and-investment-advice"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Analytics Engineer","description":"Description:\n\nAbout MLG Capital\nMLG Capital is a private real estate investment manager focused on delivering long-term, tax-efficient, risk-adjusted returns through diversified real estate strategies across the United States. As our platform continues to scale nationally, we are strengthening our data and reporting capabilities to drive operational efficiency, improve decision-making, and reduce the time required to produce core business insights.\nThis is MLG’s first-ever Analytics Engineer hire, supporting the modernization of reporting, analytics, and data operations across the firm.\nRole Overview\nThis role is designed as a modern hybrid data position that sits between traditional analytics, BI development, and engineering. Rather than hiring a narrowly scoped reporting analyst or narrow data engineer, MLG Capital is adding a versatile utility player who can help move data from source to insight, supporting backend data operations, shaping analytics-ready models, and enabling high-value dashboards, reporting, and business intelligence across the firm.\nThe person in this role will partner closely with the existing BI Developer and Data Engineer to increase team throughput, improve scalability, and strengthen the foundation for current BI reporting, future AI & predictive analytics, and enterprise data initiatives. This is a strong opportunity for a high-upside mid-level experienced candidate who wants broader ownership, cross-functional exposure, and a clear path to grow with a scaling enterprise data organization.\nHow This Role Differs from a Traditional Data Analyst\n\nThis role is broader and more foundational than a traditional Data Analyst position. A traditional analyst often focuses primarily on report production, ad hoc analysis, dashboard consumption, and answering business questions using already-prepared data. This role goes further upstream and downstream: helping shape the data models, supporting pipeline and platform operations, improving data reliability, and building reusable analytics assets that make the entire organization more scalable. It bridges analytics, engineering, and business enablement by combining technical execution with stakeholder-facing problem-solving.\nKey Responsibilities\nBuild, maintain, and improve analytics-ready datasets, transformations, and data models that support reporting, dashboarding, and downstream analysis.\nSupport data pipelines, ETL/ELT workflows, and automation processes across the Microsoft ecosystem, including Azure Data Factory, Azure Functions, Azure, VS Code and others Microsoft workflows.\nPartner with the BI Developer to develop, enhance, and maintain Power BI dashboards, semantic models, recurring reporting, data export functions and self-service analytics assets.\nWork alongside the Data Engineer to troubleshoot data issues, improve data reliability, monitor pipeline health, and help scale core enterprise data architecture.\nPartner with the BI Lead to translate business requirements into clear technical requirements, data definitions, and implementation plans.\nExecute on approved requirements by building, testing, and refining data models, reporting solutions, and supporting workflows in partnership with the BI Lead.\nContribute to data quality, governance, lineage, and documentation efforts that improve trust, auditability, and long-term maintainability of enterprise data assets.\nSupport the evolution of MLG’s modern data platform, including cloud architecture, data organization standards, and scalable analytics practices.\nIdentify opportunities to reduce manual work, improve throughput, and create reusable data products that accelerate business insight delivery.\nHelp prepare the organization for more advanced analytics use cases by strengthening foundational data structures for AI, predictive analytics, and intelligent automation.\nServe as a flexible, team-oriented utility player who can shift across analytics engineering, BI support, stakeholder problem-solving, and platform operations as priorities evolve.\nIdentify gaps in data feeds and scope new sources to advance analytics capabilities.\nRecommend and implement enhancements that support the firm's strategic goals for data-driven decision-making.\nRequirements:\n\nRequired Qualifications\n3–6 years of experience in a data, BI, analytics, or analytics engineering role.\nStrong SQL skills and experience working with structured datasets, transformations, joins, and performance-conscious query design.\nHands-on experience with Power BI, including dashboard/report development and data modeling concepts.\nWorking knowledge of cloud data platforms and modern data workflows, preferably within Microsoft Azure and/or Microsoft Data Factory.\nExperience in ETL/ELT processes, data pipelines, orchestration tools, or backend data operations.\nUnderstanding of dimensional modeling, semantic modeling, and analytics engineering concepts.\nAbility to work across technical and business teams, gather requirements, define metrics, and communicate clearly with stakeholders.\nStrong problem-solving skills, intellectual curiosity, and a practical mindset for improving processes and scaling data capabilities.\n\nPreferred Qualifications\nExperience with Azure Data Factory, Azure Functions, Microsoft Fabric, Microsoft Purview, or similar modern cloud data tools.\nExperience supporting enterprise data architecture, data governance, metadata, lineage, or data quality frameworks.\nExposure to Python, automation scripting, or API-based integrations.\nExperience in financial services, real estate, asset management, or other regulated data environments.\nFamiliarity with Microsoft AI stack a plus (Foundry, CoPilot Studio, Powerautomate, PowerApps)\nFamiliarity with predictive analytics or data preparation needs for AI use cases.\nExperience working in a growing organization where adaptability, prioritization, and cross-functional collaboration are essential.\nWhat Success Looks Like in the First 12 Months\n\nWithin the first 12 months, this person is successfully contributing across the data lifecycle rather than operating in a narrow lane. They have improved or helped maintain key Data Factory pipelines and datasets, made meaningful contributions to Power BI reporting and semantic models, reduced friction or manual effort in at least a few recurring workflows, and become a trusted cross-functional partner to business stakeholders. They are helping the team move faster, with stronger data quality, clearer definitions, and better operational reliability. Just as importantly, they are helping build the data foundation needed for future AI, predictive analytics, and enterprise-scale decision support.\n\nAdditional Notes:\nPhysical Requirements: Ability to operate office machinery; including but not limited to: telephone, computer, copy machine, fax machine, printer, and mobile phone. Ability to sit for extended periods (up to 4 hours) and use a computer for up to 8 hours per day. Ability to lift up to 10 pounds on an occasional basis.\nWorking Conditions: Open office workstation environment, quiet to moderate noise levels.\nSEC Compliance: As MLG has a subsidiary Registered Investment Adviser, many employees are subject to SEC-mandated compliance requirements. As part of these requirements, employees must disclose personal brokerage accounts and financial holdings, for themselves and any household members whose investment activities they influence.\nThis information is collected solely for regulatory compliance and conflict of interest monitoring. All disclosures are handled with strict confidentiality and are accessible only to the Chief Compliance Officer and designated compliance personnel when a business or SEC related need arises\nThis description is not intended to be all-inclusive; the employee may perform other duties as required.\nAll qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, disability, sexual orientation, national origin or any other category protected by law.\nIn compliance with the Americans with Disabilities Act, a “reasonable accommodation” will be made for an individual with a known physical or mental limitation unless it would require an action of significant difficult causing undue hardship.","datePosted":"2026-07-23T15:37:34.528Z","dateModified":"2026-07-23T15:37:34.528Z","hiringOrganization":{"@type":"Organization","name":"Mlgcapital","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Brookfield","addressRegion":"WI","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"409c704598e6f3a41d85cfd2"},"url":"https://jobsearcher.com/jobs/409c704598e6f3a41d85cfd2"}}