Senior Data Engineer

Red Hat’s Global Sales Go-To-Market Strategy, Incentives & Data Analytics organization is seeking a Senior Data Engineer to design, build, and optimize scalable data solutions supporting sales, renewals, incentives, and forecasting use cases. In this role, you will be a hands-on data engineer responsible for building reliable pipelines, transforming and validating complex datasets, and applying modern data engineering practices to ensure trusted, audit-ready data products. You will work closely with analysts, product owners, and upstream platform teams to deliver high-quality datasets while leveraging automation, AI-assisted development, and modern tooling to improve speed, accuracy, and maintainability. This role emphasizes execution excellence, technical depth, and ownership of well-defined data domains, rather than broad architectural or people leadership responsibilities. What You Will Do: Core Engineering & Development Advanced SQL & Data Modeling Develop and optimize complex SQL queries across large, multi-source datasets to support analytics and reporting use cases. Build and maintain well-structured analytical models using modern ELT patterns (fact/dimension modeling, incremental loads). Apply performance tuning techniques to improve query efficiency and pipeline reliability. Python-Based Data Automation Use Python (Pandas, NumPy, and related libraries) to cleanse, reconcile, and transform raw datasets. Automate repeatable data preparation, validation, and reconciliation workflows to reduce manual effort and error rates. Pipeline Development & Orchestration Build, schedule, and monitor data pipelines using orchestration tools such as Airflow or similar frameworks. Diagnose and resolve pipeline failures, data delays, and performance issues in a production environment. Modern Data Stack & Transformation Develop and maintain transformation logic using tools such as dbt , including tests, documentation, and lineage. Work with cloud data warehouses such as Snowflake to implement scalable, cost-efficient data solutions. AI-Assisted & Intelligent Data Engineering Leverage AI-assisted tools (e.g., code copilots, LLM-based assistants) to accelerate SQL development, testing, documentation, and refactoring. Assist in implementing rule-based and AI-augmented validation checks to detect anomalies, inconsistencies, and data quality issues. Support explainable, auditable data validation patterns that complement traditional statistical methods. Data Quality, Validation & Governance Implement automated data quality checks, reconciliation logic, and exception reporting to ensure data accuracy and consistency. Participate in validation audits and support governance requirements for incentive- and finance-critical datasets. Collaboration & Delivery Partner with analysts, business stakeholders, and upstream engineering teams to understand requirements and deliver fit-for-purpose datasets. Contribute to technical design discussions and follow established architectural standards and best practices. CI/CD & Engineering Best Practices Contribute to version-controlled repositories (Git) and follow CI/CD practices for testing and deploying data pipeline changes. Write unit and integration tests to validate transformations and ensure regression-safe releases. What You Will Bring: 6–9 years of experience as a Data Engineer, BI Engineer, or Analytics Engineer working with enterprise-scale datasets. Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. Strong SQL Skills: Proven expertise in writing and optimizing complex SQL in relational or cloud data warehouse environments. Python Proficiency: Hands-on experience using Python for data transformation, automation, and validation. Modern Data Stack: Experience with Snowflake , dbt , or similar modern cloud-based data platforms. ETL / ELT Tools: Familiarity with tools such as Fivetran or comparable da ... (truncated, view full listing at source)