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

• 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.
   

• 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.
   

• 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.
   

• 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.
   

• 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.
   

• 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.
   

• 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.
   

• 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 data ingestion frameworks.

• Data Quality Mindset: Experience implementing automated data checks, reconciliations, and validation workflows.

• Execution-Focused Ownership: Able to independently deliver high-quality data solutions within defined problem spaces.

• Analytical Thinking: Strong problem-solving skills with the ability to debug complex data and pipeline issues.

• Learning Agility: Comfortable adopting new tools, AI-assisted workflows, and modern engineering practices.

• Collaboration: Effective communicator who works well with cross-functional and globally distributed teams.

• Adaptability: Thrives in a fast-paced environment with evolving priorities and multiple concurrent deliverables.

About Red Hat

Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact.

Inclusion at Red Hat
Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.

Equal Opportunity Policy (EEO)
Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.