Data Specialist

Must be local to Charlotte, NC; relocation not available. Unable to work C2C or provide sponsorship. Job Summary We are seeking a Data Analytics Specialist to support the design, build, and modernization of our enterprise data warehouse. This role will work hands‑on in Amazon Redshift, performing data modeling, SQL development, and data validation, while also partnering closely with Data Engineers to define clear, actionable pipeline specifications. The ideal candidate brings strong SQL and database experience, experience analyzing legacy SQL Server environments, and the ability to perform data cleanup and reconciliation during platform conversions. This role sits at the intersection of analytics, data engineering, and technical analysis, requiring both execution and documentation skill. Key Responsibilities Data Modeling & Warehouse Development Design and implement dimensional data models (fact and dimension tables) in Amazon Redshift. Build and maintain curated analytics datasets to support reporting and downstream consumption. Apply data warehousing best practices (star/snowflake schemas, SCD patterns, conformed dimensions). Write, optimize, and maintain complex SQL transformations in Redshift. Current State Analysis & Data Conversion Analyze existing SQL Server databases to understand current‑state schemas, relationships, and data usage. Perform data profiling, cleanup, and remediation during data warehouse migration/conversion efforts. Identify data quality issues (duplicates, nulls, type mismatches, integrity gaps) and define resolution logic. Reconcile source and target datasets to validate accuracy and completeness. Data Engineering Collaboration & Specifications Translate business and analytical requirements into technical specifications for data pipelines, including data quality and validation rules. Define acceptance criteria and edge cases. Partner with Data Engineers during backlog refinement and implementation. Support testing, validation, and production readiness of data pipelines. Documentation & Stakeholder Collaboration Create and maintain data models, data dictionaries, and transformation documentation. Communicate effectively with technical and non‑technical stakeholders. Act as bridge between business, analytics, and engineering teams. Required Qualifications 4–7 years of experience in analytics engineering, data warehousing, or a related technical data role. Strong hands‑on experience with Amazon Redshift or similar cloud data warehouse. Advanced proficiency in SQL, including complex joins, window functions, aggregations, and performance tuning. Experience working with SQL Server and relational databases in legacy or migration scenarios. Solid understanding of data modeling and data warehousing concepts. Experience collaborating with data engineering teams and supporting ETL/ELT pipelines. Strong analytical thinking and problem‑solving skills. Ability to document technical logic clearly and concisely. Preferred / Nice to Have Experience supporting data warehouse modernization or migration projects. Familiarity with AWS data services (Glue, S3, Lambda, dbt, Airflow, etc.). Exposure to data quality frameworks and reconciliation techniques. Experience working in Agile/Scrum delivery models. Knowledge of BI and analytics tools (Tableau, Power BI, Looker, QuickSight). What Success Looks Like Redshift data models are well‑designed, performant, and aligned with business needs. SQL Server legacy data is accurately analyzed, cleaned, and migrated. Data engineering teams receive clear, complete, and implementation‑ready specifications. Stakeholders trust the data produced and understand how it is derived. #J-18808-Ljbffr