Analytics Engineer

Job Title: Analytics Engineer Location: Trumbull, CT Company: Lex Products About Lex Products: Lex Products is a leading manufacturer of innovative portable power distribution and control systems designed for industrial, entertainment, and military applications. We pride ourselves on our commitment to quality, safety, and customer satisfaction, delivering reliable power solutions that meet the highest industry standards. Position Overview: We are seeking a skilled and motivated Analytics Engineer to join our growing Analytics team. The successful candidate will be responsible for designing, developing, and maintaining data pipelines, models, and analytics tools that drive key business decisions. Additionally, the role will involve leveraging data science techniques to build predictive models, perform statistical analysis, and develop machine learning solutions that enhance our product development, manufacturing processes, and business strategies. Key Responsibilities: Data Pipeline Development: Design, build, and maintain scalable data pipelines to process and analyze large volumes of data from multiple sources. Ensure data is clean, reliable, and readily available for analysis, reporting, and data science applications. Optimize data workflows for performance, cost, and maintainability. Data Modeling and Analysis: Develop and maintain data models that reflect the companys key business processes. Work with cross-functional teams to define and implement KPIs, dashboards, and reporting tools. Apply statistical methods and data science techniques to analyze complex datasets, identifying trends, patterns, and opportunities for improvement in manufacturing, product development, and sales strategies. Data Science and Machine Learning: Develop predictive models and machine learning algorithms to solve business problems and enhance decision-making processes. Collaborate with engineers and product teams to integrate data science solutions into existing products and services. Continuously explore new data science methodologies and tools to drive innovation within the company. Collaboration and Support: Partner with engineers, product managers, and business stakeholders to understand their data and analytics needs. Provide technical support and training to team members and other departments on the use of data tools, data science, and analytics best practices. Work closely with IT to ensure data infrastructure is aligned with company goals and industry best practices. Continuous Improvement: Identify opportunities to improve existing data processes, analytics tools, and data science methodologies. Stay current with industry trends, tools, and technologies in data engineering, data science, and analytics. Contribute to the development and execution of the company's data strategy. Quality Assurance: Implement data validation, testing, and documentation processes to ensure the accuracy and reliability of analytics and data science outputs. Ensure compliance with data governance policies and best practices. Qualifications: Bachelors degree in Business Analytics, Computer Science, Data Engineering, Data Science, or a related field. A Masters degree is a plus. 2+ years of experience in data engineering, data science, or a related role, preferably in a manufacturing environment. Proficiency in SQL, Python, or other programming languages for data manipulation, analysis, and data science applications. Experience with ETL tools, data warehousing, cloud platforms (e.g., AWS, Azure, Google Cloud), and machine learning frameworks. Strong understanding of data modeling, data architecture, statistical analysis, and business in elligence tools (e.g., Power BI, Tableau, Grafana). Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark) is a plus. Experience with machine learning algorithms, predictive modeling, and data science tools (e.g., TensorFlow, scikit-learn). Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills with the ability to translate technical concepts to non-technical stakeholders. Ability to manage multiple projects and prioritize tasks in a fast-paced environment.