Business Intelligence (BI) Developer — Engineering Analytics

At IEM, we’re not just building innovative electrical distribution systems, we’re shaping the future. IEM is dedicated to delivering world-class solutions for complex power needs. After 75 years, we continue to push the boundaries of what’s possible. Whether you’re an experienced professional or just starting out, you’ll have the opportunity to contribute, grow, and make a lasting impact on industries that power the world’s most dynamic markets.Location: US RemoteReports To: Manager of Business Intelligence Salary Range: $120,000 - $140,000Position Summary:This is not a dashboard delivery role. It's an analytical partnership role that requires you to build dashboards.IEM's Engineering org - electrical, mechanical, applications, and enterprise engineering across three campuses - is operating with data that doesn't always tell a complete story, and a BI portfolio that surfaces numbers without always explaining what's behind them. We need someone who can close that gap: a person who understands how engineering actually works in a make-to-order manufacturing environment, can sit with SMEs across ERP, workforce, and engineering systems to get the underlying data right, and can walk into a leadership meeting and explain why weekly engineering efficiency is tracking at below or above its target - not just show the chart.You'll own the analytics layer that connects customer order intake, engineering throughput, and production handoff. But the real job is understanding what you're looking at well enough to have an informed opinion about it.Key Responsibilities:Be a thought partner, not an order-taker. Work directly with Engineering leadership and team leads to understand the operational problems they're trying to solve - then figure out what data is needed, whether it exists, whether it's accurate, and what it means. Diagnose, don't just report. When scheduling backlogs are growing, or mechanical lead times spike for certain product lines, or change notice on-time rates are lagging; your job is to get to the bottom of it, not hand back a chart and say, "that's what the data shows." Clean up the data foundation. Work hands-on with SMEs across ERP, HR systems, engineering milestone tracking, and drawing review systems to identify data quality issues, misaligned definitions, and gaps that undermine confidence in the numbers. Fix them, document them, and prevent them from recurring. Instrument the engineering process end-to-end. Track pipeline health, engineering phase lead times, and per-engineer performance metrics with enough context that leadership understands the drivers, not just the outputs. Support mechanical design operations. Give the mechanical team real-time visibility into scheduling KPIs; open order value, late orders, upcoming due dates, drawing review throughput, and help them understand what's causing delays when they occur. Build and maintain Tableau dashboards that give engineering teams what they need at the right level of detail - executive scorecards, operational queue views, and order-level drill-throughs - with the analytical narrative baked in, not left for the reader to guess. Work with data engineering to develop Snowflake/dbt data models that integrate ERP order data, HR workforce data, and engineering milestone data into a clean, trustworthy analytical layer that the team can build on. Qualifications:3+ years of BI or analytics experience in a manufacturing environment - engineer-to-order or make-to-order strongly preferred. You need to already understand what engineering transfers, change notices, and as-builts mean in context. Demonstrated experience working across multiple source systems and with the SMEs who own them, including getting into data quality issues, reconciling definitions, and building trust in the numbers before building dashboards on top of them. Analytical depth, not just visualization skill. You can take a metric that looks wrong, form a hypothesis about why, pull the data to test it, and communicate a clear finding to a non-technical stakeholder. Consultative instincts. You push back when the question being asked isn't the right question. You surface what leadership needs to know, not just what they asked for. Advanced Tableau, strong SQL, and familiarity with Snowflake and dbt or a demonstrated ability to get there fast. Background in electrical or mechanical engineering, or years of embedded experience working inside engineering teams, is heavily weighted in our evaluation. Bachelor's degree in Data Analytics, Engineering, Computer Science, or a related field. Why IEMIEM's engineering data tells a real operational story - multi-stage design handoffs across electrical and mechanical teams, 200+ engineers across three campuses, and constant pressure to release against customer production schedules. The numbers are live, the stakes are concrete, and there's meaningful work to do in getting the data foundation right before layering more analytics on top of it. If you want