Technical Application Manager-Data Analytics

As a Technical Application Manager within the GCS Analytics POD team, your role will involve overseeing the portfolio of Connected Truck applications to ensure operational excellence, integration, and alignment with organizational objectives. Your strong technical expertise and leadership skills will be crucial in managing complex systems across global teams, including outsourced partners. Key Responsibilities: - Application Operations & Monitoring: Ensure the smooth functioning of applications, proactively monitor performance, and address issues promptly. - Component Insight: Maintain dashboards and reports for data quality tracking. - Data-Driven Decision Making: Utilize analytics to identify issues, propose solutions, and measure improvements based on truck data, while also identifying anomalies or missing information and assessing their impact on customers. - Prioritization & Planning: Apply MoSCoW methodology and other frameworks to manage requirements and deliverables effectively. - Problem Resolution & Mentorship: Lead structured problem-solving approaches and mentor team members. - Cross-Cultural Collaboration: Facilitate the integration of outsourced teams through cultural awareness, structured onboarding, and continuous feedback. - Stakeholder Communication: Maintain transparent and timely communication with internal and external stakeholders. - Continuous Improvement: Stay updated on emerging technologies and best practices relevant to the team and department. Identify instances where automation or innovative solutions can improve operations activity. - Vendor Management: Manage relationships with third-party vendors to ensure service requirements are met. - Risk Assessment: Proactively identify technical and operational risks related to applications through data analysis and dashboard. Essential Skills & Qualifications: - Technical Experience: 7 years of technical support experience in an IoT environment. Proven automotive knowledge and basic understanding of functional vehicle behavior. Fundamental understanding of data streaming. Solid knowledge of high-frequency data transmission concepts such as Confluent, Apache Kafka. Working knowledge of CAN networks, e.g., dbc-files, can-interfaces, wiring harnesses, hex and binary calculation. Understanding of SAE J1939 and FMS standards. - Analytical & Problem-Solving: Solid analytical and problem-solving skills with a structured approach to issue resolution. Demonstrated ability to leverage data analytics tools and techniques. Strong data analysis skills in SQL, Python, Power BI, Snowflake, Tableau. - Digital & Security Awareness: Ability to demonstrate information analysis and leverage current digital tools safely. Understanding of Cybersecurity principles and GDPR core themes. - Tools: Proficiency in Microsoft Office tools (Visio, Word, Excel, PowerPoint). - Excellent communication skills for cross-functional and multicultural teams. - Ownership: Self-driven, proactive, and able to influence, mentor, and lead in a matrix organization. - Education: Bachelors or Masters degree in Computer Science, Engineering, or related field. No additional details about the company were provided in the job description. As a Technical Application Manager within the GCS Analytics POD team, your role will involve overseeing the portfolio of Connected Truck applications to ensure operational excellence, integration, and alignment with organizational objectives. Your strong technical expertise and leadership skills will be crucial in managing complex systems across global teams, including outsourced partners. Key Responsibilities: - Application Operations & Monitoring: Ensure the smooth functioning of applications, proactively monitor performance, and address issues promptly. - Component Insight: Maintain dashboards and reports for data quality tracking. - Data-Driven Decision Making: Utilize analytics to identify issues, propose solutions, and measure improvements based on truck data, while also identifying anomalies or missing information and assessing their impact on customers. - Prioritization & Planning: Apply MoSCoW methodology and other frameworks to manage requirements and deliverables effectively. - Problem Resolution & Mentorship: Lead structured problem-solving approaches and mentor team members. - Cross-Cultural Collaboration: Facilitate the integration of outsourced teams through cultural awareness, structured onboarding, and continuous feedback. - Stakeholder Communication: Maintain transparent and timely communication with internal and external stakeholders. - Continuous Improvement: Stay updated on emerging technologies and best practices relevant to the team and department. Identify instances where automation or innovative solutions can improve operations activity. - Vendor Management: Manage relationships with third-party vendors to ensure service requirements are met. - Risk Assessment: Proactively identify technical and operational risks related