Location: US Remote

Reports To: Manager of Business Intelligence 

Salary Range: $120,000 - $140,000

Position 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 IEM

IEM'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 a role where your value is in what you understand, not just what you build, this is it.

Compensation actually offered for this role will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.

Location: US Remote

Reports To: Manager of Business Intelligence

Salary Range: $120,000 - $140,000

Position Summary:

We're looking for a BI Developer who speaks the language of the shop floor. You'll be embedded with our manufacturing teams in Jacksonville, Vancouver, and Fremont; turning raw production data into the dashboards and insights that plant managers and operations leaders rely on every day to keep lines moving and quality high.

This isn't a back-office analytics role. You'll be deep in throughput data, downtime root causes, scrap trends, and on-time delivery metrics, and you'll build the tools that make those numbers actionable in real time.

Key Responsibilities:

· Own end-to-end development of Tableau dashboards tracking manufacturing KPIs — throughput, cycle time, OTD, scrap rates, WIP, capacity, and backlog.

· Work with Data Engineering to build and maintain Snowflake/dbt data models that unify ERP, MES, and shop floor systems into a single source of truth for operations leadership.

· Sit alongside plant managers and production supervisors to understand their toughest data problems and build solutions they'll use.

· Design and lead Tableau training programs for manufacturing and ops staff, turning data skeptics into daily dashboard users.

· Support lean and continuous improvement initiatives with data, identifying waste, modeling capacity scenarios, and tracking kaizen results.

Qualifications:

· 3+ years of BI experience in a manufacturing or industrial environment, you understand production workflows, not just data models.

· Advanced Tableau skills. A plush would be to have a portfolio of manufacturing KPI dashboards (throughput, OEE, quality, delivery).

· Strong SQL and Snowflake skills; hands-on experience with ETL tools and ERP/MES data sources.

· Ability to translate shop-floor conversations into data requirements, and present findings back in plain language.

· Lean/Six Sigma exposure or genuine curiosity about operational efficiency is a plus.

· Bachelor's degree in Data Analytics, Operations, Engineering, or a related field.

Why IEM

At IEM, analytics isn't a support function, it's a competitive advantage. You'll have direct access to operations leaders and real influence over how our plants run. If you want your work to matter on the manufacturing floor, not just in a report, this is your role.

Compensation actually offered for this role will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.

Location: US Remote

Reports To: Director of Data and Analytics 

Salary Range: $130,000 - $170,000

Position Summary:

The Data Engineer is a core builder on IEM’s data and analytics team. This role develops and maintains the data pipelines and transformation models that power Tableau dashboards and business decisions across the organization. Working within an established modern data stack (Fivetran, Snowflake, dbt, Tableau. The Data Engineer turns raw source data into reliable, well-tested, and well-documented analytics-ready datasets. This is a hands-on individual contributor role with real ownership of production data models and growing influence on engineering standards.

Ideal Candidate Profile

You have 3–5 years of experience building data pipelines and transformation workflows in cloud environments. You’re proficient with SQL and Python, comfortable working in Snowflake, and have hands-on experience with dbt or similar transformation tools. You write clean, tested code and take pride in documentation. You’re curious about the business context behind the data and can translate stakeholder questions into well-modeled datasets. You work well on a small team, take feedback constructively, and are eager to grow your skills in analytics engineering, data modeling, and cloud data architecture. You’re excited about AI’s role in modern data work and comfortable using AI coding assistants and agents to accelerate your output.

Key Responsibilities:

• Data Pipeline Development: Build and maintain ELT pipelines using Fivetran and custom integrations that ingest data from source systems including Procore, Salesforce, ERP platforms, and internal databases into Snowflake
• dbt Transformation Models: Develop, test, and document dbt models that transform raw data into clean, reliable datasets for analytics and reporting across Finance, Production, Supply Chain, and Engineering
• Data Modeling: Build dimensional models and staging layers following team conventions, ensuring data is structured for optimal Tableau dashboard performance and ad-hoc analysis
• Data Quality: Write and maintain dbt tests, monitor data freshness, and investigate data quality issues when they arise, owning resolution through to root cause
• Source System Integration: Work with APIs and data connectors to integrate new data sources, troubleshoot ingestion issues, and ensure reliable data flow into the warehouse
• Documentation: Maintain clear documentation for data models, pipeline configurations, and business logic so the team can understand and extend your work
• Collaboration: Partner with business stakeholders to understand data needs, clarify requirements, and deliver datasets that answer real operational questions
• Performance: Monitor query performance and pipeline efficiency, identifying opportunities to optimize warehouse costs and model run times
• Engineering Standards: Participate in code reviews, follow Git workflows and CI/CD practices, and contribute to improving the team’s development processes
• Continuous Learning: Stay current with modern data stack tools and practices, bringing ideas for improvement back to the team
• AI-Assisted Development: Use AI coding assistants and agent-based tools to accelerate pipeline development, code generation, testing, and documentation. Manage AI agents as part of your daily workflow to increase throughput and quality

Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Data Science, or related field, or equivalent experience
• 3–5 years of experience in data engineering, analytics engineering, or BI development with hands-on experience building production data pipelines
• Strong SQL skills including CTEs, window functions, complex joins, and query optimization
• Experience with Snowflake or similar cloud data warehouses
• Working experience with dbt (data build tool) for building and testing data transformation workflows
• Proficiency in Python for data processing, scripting, and API integrations
• Experience with data integration platforms such as Fivetran or similar ELT tools
• Familiarity with Tableau or similar BI tools and understanding of how data structure impacts dashboard performance
• Comfortable with Git version control and modern development workflows including code review and CI/CD
• Strong problem-solving skills with ability to debug data issues systematically
• Clear written and verbal communication skills with ability to document work and explain technical concepts to non-technical stakeholders
• Self-motivated with ability to work independently in a remote environment while collaborating across a distributed team
• Experience using AI coding assistants (e.g., Claude, GitHub Copilot) and comfort directing AI agents to perform data engineering tasks such as writing SQL, generating dbt models, and debugging pipeline issues
• Preferred: Experience with manufacturing, construction, or project management data systems such as Procore, ERP platforms, or supply chain tools
• Preferred: Familiarity with dimensional modeling concepts (star schemas, fact/dimension tables)

Location

Fully remote within the United States. May require up to 10% travel to IEM facilities for team collaboration, project kickoffs, and stakeholder meetings.