Senior Data Engineer (Hybrid Onsite)

Job summary

Job Title: Data Engineer (Hybrid Onsite) Location: Lansing, MI Duration: 12+ Months Interview Type: Virtual Interview via MS Teams video. Please use laptop and be prepared to share screen if asked. Note: - Hybrid: Resource will be working a hybrid schedule.

Qualifications

• Wednesdays and Thursdays are required onsite days.
• References are required for this position.
• Perform root cause analysis to identify systemic portfolio issues and recommend corrective actions.
• Identify risks and opportunities requiring escalation to the EPMO and DTMB executive leadership.
• Develop predictive and trend-based reporting supporting proactive portfolio management and investment decision-making.
• Business Analysis and Stakeholder Engagement
• Facilitate meetings with business stakeholders to identify reporting
• performance measures, and analytical needs.
• Gather, analyze, and document business and technical
• for enterprise reporting solutions.
• Translate business
• into technical reporting and dashboard solutions.

Responsibilities

• The Senior Data Engineer shall perform
• including, but not limited to, the following:
• Portfolio Reporting and Business Intelligence
• Design, develop, implement, and maintain enterprise dashboards, executive scorecards, and business intelligence solutions supporting statewide IT investment management.
• Develop standardized portfolio reporting supporting ITIF investments, project performance, portfolio governance, and executive oversight.
• Design meaningful Key Performance Indicators (KPIs), portfolio metrics, and visualizations that communicate organizational performance and portfolio health.
• Develop automated reporting processes that improve reporting accuracy, consistency, and timeliness.
• Produce recurring and ad hoc executive reports supporting DTMB leadership, governance bodies, and agency stakeholders.
• Data Engineering and Integration
• Develop, optimize, and maintain complex SQL queries, data models, and transformation processes.
• Design scalable data models that support enterprise reporting and analytical capabilities.
• Ensure data quality, consistency, integrity, and reliability across reporting environments.

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