Hybrid Analytics Engineer: Data Pipelines & BI

LHH; Milwaukee (WI); Hybrid; USD 85,000 - 125,000; Full time

Job summary

LHH in Greater Milwaukee area seeks a versatile data professional bridging analytics, BI development, and engineering to move data from source to insight. You will collaborate with BI Developer and Data Engineer to scale data operations, build analytics assets, and enable enterprise dashboards and advanced analytics.

This mid-level role offers broader ownership, alignment with enterprise data initiatives, and growth potential within a scaling data organization.

Qualifications

• 3–6 years in a data, BI, analytics, or analytics engineering role.
• Strong SQL skills and experience 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.

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 other 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 our 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.

Skills

SQL

Power BI

Cloud data platforms

ETL/ELT

Dimensional modeling

Stakeholder communication

Problem solving

Tools

Power BI

Azure Data Factory

Azure Functions

Microsoft Fabric

Job description

LHH in Greater Milwaukee area seeks a versatile data professional bridging analytics, BI development, and engineering to move data from source to insight. You will collaborate with BI Developer and Data Engineer to scale data operations, build analytics assets, and enable enterprise dashboards and advanced analytics.

This mid-level role offers broader ownership, alignment with enterprise data initiatives, and growth potential within a scaling data organization.