Lead Data and Systems Engineer

Job Summary:

Build Guidant Power's enterprise technology capabilities by designing and implementing a scalable data architecture that enables better decision making across the organization. This hands-on individual contributor will lead the design and implementation of Guidant's enterprise systems architecture, data warehouse, improve data quality, support system integrations, and develop reporting that enables better business decisions.

Essential Duties and Responsibilities:

Business Partnership and Data Strategy

• Develop and help execute Guidant's enterprise data architecture roadmap in partnership with leadership.

• Partner with business leaders to identify reporting, automation, and technology improvement opportunities.

• Improve master data quality by developing practical governance processes and standards.

• Evaluate new technologies and automation opportunities that improve business performance.

• Partner with software vendors, implementation partners, and consultants to deliver technology initiatives.

Data Engineering & Architecture

• Design and implement Guidant's initial enterprise data warehouse, expanding capabilities over time.

• Partner with business leaders to build, prioritize and implement data warehouse use cases

• Develop and maintain ETL/ELT pipelines across enterprise applications.

• Design scalable data models that provide a single source of truth for reporting.

• Implement and improve integrations among NetSuite, CRM, HRIS, scheduling, and future enterprise systems.

• Develop best practices for data quality, documentation, security, and reporting consistency.

• Support the integration of acquired companies into Guidant's reporting environment.

• Understands roles, permissions, segregation of duties, API credentials, PII, audit trails, backups, and basic cybersecurity principles.

Business Intelligence & Reporting

• Partner with business leaders to understand reporting needs and develop meaningful KPIs and dashboards across Finance, Sales, Operations, HR, etc.

• Ensure consistent business definitions across reports and dashboards.

• Continuously improve reporting through automation and self-service analytics.

Experience:

3-8 years of experience in data engineering, analytics engineering, business intelligence, IT security or enterprise data architecture, with progressively increasing responsibility.

Strong technical expertise in SQL, Python (or equivalent), ETL/ELT development, APIs/REST integrations, integration platforms (iPasS), data modeling, enterprise data warehouse concepts, and data security.

Experience building or supporting enterprise data warehouses and scalable reporting solutions.

Experience developing business intelligence solutions using Power BI, Tableau, or similar platforms

Experience partnering with business leaders, software vendors, implementation consultants, and third-party developers to deliver technology initiatives.

NetSuite experience is strongly preferred. Experience in professional services, engineering, industrial services, field service organizations, or multi-location businesses is also preferred.

Personal
Characteristics

Leadership & Influence: Establishes credibility across leadership and frontline employees. Gains cooperation across Finance, Operations, Sales, HR, and external partners without relying on direct authority.

Business Curiosity: Demonstrates a genuine desire to understand how the business operates. Learns workflows, asks thoughtful questions, and translates operational challenges into technology solutions.

Continuous Improvement: Consistently seeks better ways of working by simplifying processes, improving data quality, eliminating manual work, and leveraging technology to increase organizational effectiveness.

Systems Thinker: Understands how enterprise systems, business processes, and data architecture work together. Enjoys solving complex operational challenges through technology and data.

Technical Excellence: Produces reliable, scalable technical solutions while continuously learning modern data engineering best practices.

Communication: Explains complex technical concepts in language appropriate for executive leadership, business users, and technical partners. Builds trust through clear communication and collaboration.

Ownership & Initiative: Takes ownership of outcomes, proactively identifies opportunities for improvement, and consistently follows through on commitments.