JOB TITLE: GEN AI Engineer LOCATION: Remote (excluding CA and HI) — must be available during Eastern Time hours DURATION: Contract-to-hire PAY RANGE: $45–47/hr REQUIREMENTS: • 3–5 years of experience as a data engineer • Strong background in ETL processes and data warehousing • Proficient in SQL and Python • Experience with data integration tools such as Informatica, Talend, or Apache Airflow • Technical expertise in: • Data processing and pipeline development • Relational and NoSQL databases • Cloud platforms (AWS, Google Cloud) • Big data technologies (e.g., Hadoop, Spark) • Familiarity with: • Programming languages (Python, Java) • Data manipulation tools (e.g., Pandas, SQL) • Version control systems • Knowledge of machine learning fundamentals, model deployment, and data preparation • Understanding of data governance, quality, and security • Familiarity with data privacy and regulatory compliance in AI applications • Strong problem-solving, communication, and collaboration skills • Bachelor's degree in Computer Science, Engineering, or a related field Key Skills/Experience: • Pandas, SQL, Java • Relational database experience • AI and ML experience in contact center environments • Measuring customer feedback data • Using Python for ETL, data integration, or modeling POSITION OVERVIEW: This role involves developing and maintaining robust data pipelines that support advanced analytics and AI initiatives. The engineer will extract, transform, and load data from multiple systems into data warehouses and data lakes, ensuring data quality and accessibility for analysis. The position supports operational efficiency and improved customer experience through close collaboration with business and analytics teams. PRIMARY RESPONSIBILITIES: Data Engineering: • Build and manage data pipelines using modern tools and frameworks • Extract data from various platforms including CRM and call center systems • Standardize and transform data for consistency and usability • Load data into storage environments for analytics and reporting Data Modeling: • Create data models that reflect business requirements • Implement dimensional models for reporting and analysis Data Integration: • Combine data from multiple sources, resolving inconsistencies • Maintain ETL workflows to automate ingestion and transformation Data Quality: • Monitor data accuracy and completeness • Design and enforce data validation protocols Performance Optimization: • Tune pipelines and queries for optimal performance • Identify and implement improvements to accelerate data delivery Collaboration: • Translate business needs into technical solutions through teamwork with analysts and stakeholders • Partner with data science teams to support machine learning and AI initiatives