Responsible for developing and maintaining ETL processes to extract data from various sources, transform it based on business requirements, and load it into target databases or data warehouses. This role involves ensuring data accuracy, optimizing performance, and collaborating with team members and stakeholders to support reliable and efficient data integration workflows. \This is an ONSITE position in Latham, NY\ Relocation assistance is not supported for this role. JOB RESPONSIBILITIES - ETL Process Development - Design, develop, and maintain ETL pipelines to extract, transform, and load data from various structured and unstructured sources. - Implement both incremental and full data load strategies, ensuring data quality and consistency. - Build reusable components to standardize and streamline ETL processes. - Data Modeling & Integration - Support the development and optimization of data models for data warehouses and operational data stores. - Ensure appropriate use of indexing, partitioning, and other techniques to improve performance. - Data Quality & Validation - Develop and implement data validation, error handling, and reconciliation processes to ensure accuracy and completeness. - Collaborate with data governance teams to support data quality and compliance initiatives. - Performance Tuning - Monitor ETL job performance and implement improvements such as query tuning and parallel processing to optimize efficiency. - Collaboration - Work closely with analysts and business users to understand data requirements and deliver appropriate solutions. - Translate business needs into technical specifications and deliverables. - Documentation - Maintain clear documentation for ETL processes, data flows, and technical specifications to support ongoing maintenance and knowledge transfer CORE SKILLS/COMPETENCY REQUIREMENTS - Programming Languages: Advanced knowledge of SQL, with proficiency in T-SQL. - ETL Tools: Experience with industry-standard ETL tools (e.g., SSIS, Azure Data Factory, Informatica, Pentaho, etc. ) - Database Management: Understanding of database concepts (relational and dimensional), including database design, query optimization, and performance tuning - Data Warehousing: Knowledge of data warehouse concepts, dimensional modeling, and star schema design - Data Quality Techniques: Familiarity with data cleansing, data validation, and data profiling methods - Problem-solving: Ability to troubleshoot complex data issues, identify root causes, and implement effective solutions - Bachelor's degree in computer/data science or similar field of study preferred