Summary: Analytics Engineers bridge data engineering and analysis, transforming and documenting data for end-users, applying established standards and continuously learning new technologies. Highlights: 1. Bridge between data engineers and data analysts 2. Focus on technical expertise and continuous learning 3. Collaborate with diverse teams and stakeholders Analytics Engineers act as a bridge between data engineers and data analysts, focusing on transforming, testing, deploying, and documenting data to make it available, organized, and easier to analyze for end\-users. They are focused on developing their technical expertise in the data domain and contributing to project deliverables under the guidance of senior analytics engineers or data scientists. They apply established data governance standards, analytical methodologies, and tools to process data and generate insights. They are expected to learn continuously, stay updated with relevant data technologies, and collaborate effectively with team members, powertrain calibration engineers, and other data stakeholders. Their role involves building and maintaining segments of data pipelines, performing data analysis, creating reports and dashboards based on specifications, and supporting the implementation of analytical solutions. * **Education** **:** * **Required:** Bachelor's degree in Computer Science , Data Science, Engineering, Mathematics, Statistics, or a similar related field. * **Experience** **:** * 1\-5\+ years of relevant experience in data engineering, data analysis, or data science roles, with a focus on delivering data\-driven insights and solutions. * Demonstrated hands\-on experience with data processing, analysis, and visualization. * Experience within the automotive industry, particularly with powertrain data, is highly advantageous. * **Hard** **Skills** **:** * **Programming \& Scripting:** Proficiency in Python for data analysis and automation. Familiarity with other programming languages (e.g., MATLAB/Simulink) is a plus. * **Data Querying:** Strong SQL skills for data extraction, manipulation, and analysis. * **Data Engineering Fundamentals:** Understanding of ETL/ELT concepts, data modeling, and data warehousing. * **Data Visualization Tools:** Proficiency with tools like Tableau, PowerBI , or Qlik. * **Cloud Data Services:** Awareness and practical experience with cloud platforms (e.g., Azure, AWS, GCP) and their data services. * **Data Analysis:** Proven ability to analyze large datasets to inform engineering decisions. * **Version Control:** Familiarity with version control systems (e.g., Git). * **Soft** **Skills** **:** * **Communication:** High\-level verbal and written communication skills, with the ability to clearly document work and present technical findings. * **Problem Solving:** A diligent, passionate, and accountable approach to troubleshooting complex technical challenges. * **Initiative** **\& Adaptability** **:** High self\-learning skills with the ability to quickly master new technologies and processes with minimal supervision. * **Teamwork \& Collaboration:** Excellent teamwork and interpersonal skills, with the ability to collaborate effectively in a global team environment. * **Attention to Detail:** Detail\-oriented approach to ensure data quality and accuracy. * **Data Problem Solving \& Task Execution:** Engage in data\-related tasks within a defined scope, often involving known challenges in data acquisition, cleaning, processing, or basic modeling. Apply data handling skills and analytical techniques to complete assigned work. * **Data Project Contribution \& Support:** Contribute to specific data project deliverables, supporting senior team members in areas such as data pipeline development, data quality checks, model testing, or dashboard creation. * **Application of Data Standards \& Processes:** Follow established standards for data governance, data quality, metadata management, and analytical best practices in daily work. Utilize existing data tools and platforms effectively as directed . * **Data Skill Development \& Application:** Actively develop technical skills in data engineering, analytics, cloud platforms, and foundational data science methodologies, especially as they apply to powertrain data. Apply learned knowledge to assigned data tasks and seek opportunities for growth. * **Effective Data Solution Implementation:** Implement components of data\-driven solutions, such as scripts for data transformation, segments of automated data pipelines, or basic analytical dashboards, according to project plans and technical specifications. * **Technical Proficiency (Data Stack \& Powertrain Context):** Develop and apply proficiency in data engineering principles (e.g., ETL/ELT concepts, basic data modeling), data querying (SQL), programming for data analysis (e.g., Python), and data visualization tools (e.g., Tableau, PowerBI ). Apply this proficiency with an understanding of powertrain data sources (e.g., calibration files, test data, OBD data) and their relevance. * **Data Pipeline \& Model Support:** Support the development, testing, and maintenance of data pipelines and analytical models. Execute validation procedures for data integrity, reliability, and basic model accuracy under supervision. * **Trend Analysis and Data Visualization:** Analyze powertrain datasets to identify trends, anomalies, and patterns as directed. Create reports and dashboards to visualize data insights based on provided requirements. * **Alignment with VPSE Data Objectives:** Understand and align work with the data\-related objectives of their team and VPSE, supporting data\-driven decision\-making through diligent execution of assigned tasks. * **Cross\-Functional Data Interaction:** Work effectively with immediate team members, calibration engineers, controls engineers, testing teams, and IT on specific data\-related tasks and to understand data requirements. * **Data Product Support \& Documentation:** Assist in the rollout of new data reports or tools and contribute to technical documentation related to data processes, pipelines, and analytical findings. * **Task Management \& Adherence to Guidelines:** Manage assigned data tasks effectively to meet deadlines and adhere to data security, quality, and Ford OS Behavior guidelines.