Analytics Engineer II – Product Lifecycle (Remote)

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Overview

The Analytics Engineer II – Product Lifecycle at Collins Aerospace is a vital role designed to enhance product development through data-driven decision-making. This position involves leveraging analytical techniques to improve product lifecycles, ensuring optimal performance, and contributing to innovation. The successful candidate will collaborate closely with cross-functional teams to gather and analyze data, thereby providing actionable insights that facilitate development efficiency and product quality.

Job Responsibilities

• Design and develop analytic dashboards to monitor product lifecycle metrics.
• Conduct deep-dive analyses to identify trends, risks, and opportunities related to product performance.
• Collaborate with product management and engineering teams to define key performance indicators (KPIs) and measurement frameworks.
• Automate data collection and reporting processes to enhance operational efficiency.
• Present findings and recommendations to stakeholders across various departments.
• Utilize statistical analysis and modeling techniques to optimize product decision-making.
• Stay updated on industry trends and best practices related to analytics and product lifecycle management.

Qualifications

• Bachelor’s degree in a related field (e.g., Data Analytics, Statistics, Engineering, Computer Science).
• Minimum of 3 years of experience in analytics or data engineering roles.
• Proficiency in analytical tools and languages, such as SQL, Python, or R.
• Experience with data visualization platforms (e.g., Tableau, Power BI).
• Familiarity with product lifecycle management concepts and methodologies.
• Strong problem-solving skills with a focus on data-driven insights.
• Excellent communication skills, with the ability to articulate complex findings to diverse audiences.

Technologies & Tools

In this role, candidates will utilize a variety of technologies and tools to execute their responsibilities effectively. Key technologies include SQL databases for data retrieval and manipulation, statistical analysis tools such as R and Python for modeling, and visualization software like Tableau or Power BI for presenting data-driven insights. Additionally, familiarity with cloud computing platforms (e.g., AWS, Azure) and applications pertaining to product lifecycle management might also be beneficial.

Ideal Candidates

The ideal candidate for the Analytics Engineer II position possesses a strong analytical mindset, critical thinking skills, and an innate curiosity about data and product lifecycles. They are detail-oriented, possess a collaborative spirit, and are driven by the pursuit of continuous improvement. Successful candidates thrive in dynamic environments and can effectively communicate technical concepts to non-technical stakeholders, making them valuable assets to cross-functional teams.