Analytics Engineer

About The Team

​​​​​​Our client is a growing investment management organization focused on delivering long-term value through diversified investment strategies across the United States. As the business continues to scale, they are investing in modernizing their data and analytics capabilities to improve operational efficiency, enhance decision-making, and accelerate access to critical business insights.

This is the organization’s first dedicated Analytics Engineer hire, created to help advance reporting, analytics, and data operations while building a stronger foundation for future growth and innovation.

Role Overview 

This 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, we’re 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. 

The 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. 

How This Role Differs from a Traditional Data Analyst 

This 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. 

 Key Responsibilities  

• Build, maintain, and improve analytics-ready datasets, transformations, and data models that support reporting, dashboarding, and downstream analysis. 
• Support 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. 
• Partner with the BI Developer to develop, enhance, and maintain Power BI dashboards, semantic models, recurring reporting, data export functions and self-service analytics assets. 
• Work alongside the Data Engineer to troubleshoot data issues, improve data reliability, monitor pipeline health, and help scale core enterprise data architecture. 
• Partner with the BI Lead to translate business requirements into clear technical requirements, data definitions, and implementation plans. 
• Execute on approved requirements by building, testing, and refining data models, reporting solutions, and supporting workflows in partnership with the BI Lead. 
• Contribute to data quality, governance, lineage, and documentation efforts that improve trust, auditability, and long-term maintainability of enterprise data assets. 
• Support the evolution of MLG’s modern data platform, including cloud architecture, data organization standards, and scalable analytics practices. 
• Identify opportunities to reduce manual work, improve throughput, and create reusable data products that accelerate business insight delivery. 
• Help prepare the organization for more advanced analytics use cases by strengthening foundational data structures for AI, predictive analytics, and intelligent automation. 
• Serve as a flexible, team-oriented utility player who can shift across analytics engineering, BI support, stakeholder problem-solving, and platform operations as priorities evolve. 
• Identify gaps in data feeds and scope new sources to advance analytics capabilities. 
• Recommend and implement enhancements that support the firm’s strategic goals for data-driven decision-making. 

Required Qualifications 

• 3–6 years of experience in a data, BI, analytics, or analytics engineering role. 
• Strong SQL skills and experience working with structured datasets, transformations, joins, and performance-conscious query design. 
• Hands-on experience with Power BI, including dashboard/report development and data modeling concepts. 
• Working knowledge of cloud data platforms and modern data workflows, preferably within Microsoft Azure and/or Microsoft Data Factory. 
• Experience in ETL/ELT processes, data pipelines, orchestration tools, or backend data operations. 
• Understanding of dimensional modeling, semantic modeling, and analytics engineering concepts. 
• Ability to work across technical and business teams, gather requirements, define metrics, and communicate clearly with stakeholders. 
•  Strong problem-solving skills, intellectual curiosity, and a practical mindset for improving processes and scaling data capabilities.  

Preferred Qualifications 

• Experience with Azure Data Factory, Azure Functions, Microsoft Fabric, Microsoft Purview, or similar modern cloud data tools. 
• Experience supporting enterprise data architecture, data governance, metadata, lineage, or data quality frameworks. 
• Exposure to Python, automation scripting, or API-based integrations. 
• Experience in financial services, real estate, asset management, or other regulated data environments. 
• Familiarity with Microsoft AI stack a plus (Foundry, CoPilot Studio, Powerautomate, PowerApps) 
• Familiarity with predictive analytics or data preparation needs for AI use cases. 
• Experience working in a growing organization where adaptability, prioritization, and cross-functional collaboration are essential. 

What Success Looks Like in the First 12 Months 

Within 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.