Data Analytics Lead Specialist Engineer

Job Title: Data Analytics Lead Specialist Engineer Location: Little Rock, Arkansas Experience Required: 12+ years Employment Type: Contract Interview Type: In-Person or Webcam Job Description The Data Analytics Lead Specialist Engineer will be responsible for driving advanced analytics initiatives and delivering data-driven insights that support business decision-making. This role involves leading complex data engineering and analytics projects, collaborating with cross-functional teams, and ensuring that analytical solutions are scalable, reliable, and aligned with organizational goals. The ideal candidate should have extensive experience in data architecture, analytics modeling, data quality practices, and leadership of analytics teams. Key Responsibilities Lead the strategy, design, and implementation of analytics solutions across enterprise environments. Collect, analyze, and interpret large structured and unstructured datasets to extract meaningful insights. Develop and optimize data pipelines, ETL processes, and analytical models. Guide data visualization efforts and deliver analytical dashboards and reporting solutions for stakeholders. Work closely with business units, product teams, and IT teams to understand analytical needs and translate requirements into technical solutions. Lead, mentor, and provide technical direction to analytics and data engineering teams. Establish best practices for data governance, data quality, security, and metadata management. Conduct advanced analytics, predictive modeling, forecasting, and statistical analysis to support business planning. Evaluate new technologies and analytics tools for continuous improvement. Troubleshoot and resolve analytics platform performance issues and data inconsistencies. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, Statistics, Engineering, or a related field. 12+ years of experience in data analytics, data engineering, or business intelligence. Strong expertise in SQL, Python, R, or other analytics languages. Hands-on experience with data warehousing technologies and cloud platforms such as AWS, Azure, or Google Cloud. Proficiency in ETL tools and frameworks such as Informatica, Talend, or Apache Spark. Demonstrated experience with BI and reporting tools such as Power BI, Tableau, Qlik, or Looker. Strong background in statistical modeling, machine learning, and predictive analytics. Proven ability to lead enterprise analytics initiatives and manage cross-functional teams. Strong analytical thinking, problem-solving, and communication skills. Preferred Skills Experience working in Agile or DevOps environments. Knowledge of big data technologies such as Hadoop, Kafka, Hive, or Snowflake. Experience in implementing data governance, MDM, and metadata management solutions. Familiarity with AI and automation capabilities such as NLP, generative AI, or advanced ML frameworks. Experience in an enterprise-level or large-scale corporate environment. Strong presentation skills for technical and non-technical audiences.