Infotree Global Solutions

Bachelor's degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Economics, Finance, Industrial/Organizational Psychology and Econometrics, etc.). and seven years of experience working in a data processing, analytical or computer programming function; OR Master's degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Economics, Finance, Industrial/Organizational Psychology and Econometrics, etc.). Intermediate-level proficiency in business intelligence tools (e.g., Client Power Platform (e.g., Power BI, Power apps, Power Automate), Tableau, SAP Business Objects (BOBJ) or data blending. Intermediate-level proficiency with a Big Data platform (Hadoop, AWS, Azure, Databricks), including data extraction and connection to the platform for analytics.. Experience with SAP Business Intelligence Tools and SAP CRM, ISU and BW data.

data engineer

Role: Sr. Data Engineer Location: Detroit, MI OR Remote Duration: 12+ Months Job Summary Leads business units with analytics and data integration methods to automate the data collection, transformation, storing, delivery and reporting processes. Leads large business unit or enterprise-level data projects with broad responsibilities for translating business requirements into data and analytical constructs for effective decision making. Mentors less-experienced team members to optimize data retrieval and processing as well as performance tuning and design of the pipeline for delivery of the data to feed down-stream analytics, machine learning modeling (including feature engineering), and reporting, including evaluation of alternative approaches. Communicates effectively to technical and non-technical stakeholders with strong domain expertise and business acumen. Key Accountabilities Leads data projects and collaborates with cross-functional stakeholders to understand business needs, formulate complete end-to-end solution that includes business requirements, data gathering (including data structure design) to feed downstream analytics, machine learning modeling (including feature engineering), and reporting, including design data processing pipeline and develop prototypes and proof-of-concepts. Develops complex data sets and automated pipelines to support key decisions to improve safety, employee engagement, compliance rates, operational efficiency, product quality, and customer satisfaction, and other key performance metrics. Prepares and delivers insightful presentations and actionable recommendations. Educates leaders and other employees on complex data and analytical findings in basic terms and with storytelling and data visualization. Create and maintain optimal data transformation (ETL) processes. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. Designs and implements data process pipelines (in on-premise or Cloud platforms) required for optimal extraction, transformation, and loading of data from a wide variety of data sources. Deploy and automate Machine Learning Models in a data environment (e.g., SQL server, Cloud platform, on-premise servers and machines) and include workflow orchestration, scheduling and implementing advanced data processing and data delivery tools like docker for containerization. Researches and applies industry best practices to advance the organization's data processes. Minimum Education And Experience Requirements This is a dual-track base requirement job; education and experience requirements can be satisfied through one of the following two options: Bachelor's degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Economics, Finance, Industrial/Organizational Psychology and Econometrics, etc.) and seven years of experience working in a data processing, analytical or computer programming function; OR Master's degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Economics, Finance, Industrial/Organizational Psychology and Econometrics, etc.) and five year of experience working in a data processing, analytical or computer programming function. Preferred Other Qualifications: Business domain knowledge SQL Database design and query optimization experience Other Requirements Expert-level programming skills in SQL and/or C/C++/C#, PHP, Java, Python, R, ASP, or SAS. Intermediate-level experience in articulating business questions, pulling data from relational databases (e.g., SAP BW, ORACLE, SQL SERVER). We have this item in the data scientist jobs as well. Intermediate-level proficiency in business intelligence tools (e.g., Client Power Platform (e.g., Power BI, Power apps, Power Automate), Tableau, SAP Business Objects (BOBJ) or data blending tools such as Alteryx. Intermediate-level proficiency with a Big Data platform (Hadoop, AWS, Azure, Databricks), including data extraction and connection to the platform for analytics. Experience with SAP Business Intelligence Tools and SAP CRM, ISU and BW data. Ability to adapt to changing software platforms for retrieving, analyzing, sharing, and visualizing data Capable of recognizing the best analytical tools to apply to problems dependent on problem requirements Create automated dashboards on findings and makes recommendations to leadership based on results Analytical, problem solving, planning, and decision-making skills with ability to identify key issues from a broad range of alternatives and recommend optimal solutions and best course of action Self-starter and quick learner; advances self and other's knowledge and skillset in business processes, data science, and new analytical frameworks, technologies and applications Demonstrated interpersonal, analytical and problem-solving skills, including ability to communicate (in both verbal and written form) technical information and complex data analytics to a non-technical audience in a clear and concise manner

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