Principal Data Engineer

Red Hat’s Global Sales Go-To-Market Strategy, Incentives & Data Analytics organization is seeking a Principal Data Engineer to serve as a technical leader for complex, business-critical data products supporting sales, renewals, incentives, forecasting, and executive reporting.
This role goes beyond individual pipeline delivery to own end-to-end data domains, define technical patterns, and raise engineering standards across the team. You will design resilient, scalable, and audit-ready data solutions, lead complex data modeling and validation efforts, and influence how modern data engineering and AI-assisted techniques are applied across the organization.
The Principal Data Engineer is expected to combine deep hands-on engineering expertise with technical decision-making, mentorship, and cross-team influence, without direct people management responsibility.

What You Will Do:

Technical Leadership & Ownership

• Act as the technical owner for one or more critical sales, renewals, or incentives data domains.

• Define and evolve data engineering standards, design patterns, and best practices across pipelines, transformations, and validation layers.

• Lead technical design reviews and guide architectural decisions for complex or high-risk data initiatives.

• Mentor senior and mid-level engineers through code reviews, design feedback, and hands-on problem solving.

Advanced SQL, Data Modeling & Analytics Engineering

• Design and implement enterprise-grade analytical data models (facts, dimensions, snapshots, aggregates) supporting multi-year trend analysis and forecasting.

• Write, optimize, and standardize high-complexity SQL across large, multi-source datasets with a focus on performance, scalability, and cost efficiency.

• Establish modeling strategies for incremental processing, slowly changing dimensions, and historical restatements.

• Partner with analytics and finance stakeholders to ensure models are business-accurate, explainable, and decision-ready.

Python Automation & Data Engineering

• Build robust Python frameworks for data transformation, reconciliation, validation, and anomaly detection.

• Automate complex, repeatable workflows that reduce manual intervention and operational risk.

• Develop reusable utilities and libraries to improve consistency and speed across data pipelines.

Pipeline Architecture & Orchestration

• Design and maintain production-grade pipelines using orchestration tools such as Airflow or similar frameworks.

• Implement proactive monitoring, alerting, and recovery strategies for data freshness, completeness, and correctness.

• Lead root-cause analysis for complex pipeline or data integrity issues and drive long-term fixes.
   

Modern Data Stack & Cloud Warehousing

• Architect scalable solutions on Snowflake or similar cloud data warehouses, balancing performance, cost, and governance.

• Lead development of transformation logic using dbt, including testing, documentation, lineage, and deployment workflows.

• Influence ingestion strategies using ELT tools (e.g., Fivetran or equivalents) and custom ingestion patterns where required.
   

AI-Assisted & Intelligent Data Engineering

• Champion the use of AI-assisted development (LLM copilots, code generation, refactoring, and documentation) to improve engineering productivity.

• Design and implement rule-based and AI-augmented data validation frameworks to detect anomalies, inconsistencies, and data drift.

• Apply explainable AI patterns that augment—not replace—traditional statistical and deterministic validation methods.

• Ensure all AI-enabled workflows remain auditable, transparent, and compliant with incentive and finance governance.

Data Quality, Governance & Audit Readiness

• Define and enforce data quality SLAs, reconciliation frameworks, and exception handling for executive- and finance-critical datasets.

• Lead validation efforts for audits, quarter-end closes, and planning cycles.

• Partner with upstream platform, governance, and finance teams to ensure lineage, traceability, and policy alignment.

CI/CD & Engineering Excellence

• Establish and enforce CI/CD standards for data pipelines, including testing, deployment, rollback, and versioning.

• Design comprehensive unit, integration, and data validation tests to ensure regression-safe releases.

• Drive continuous improvement in code quality, documentation, and operational resilience.

What You Will Bring:

• 9–12+ years of experience as a Data Engineer, Analytics Engineer, or BI Engineer working with enterprise-scale, business-critical data.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.

• Advanced SQL Expertise: Deep mastery of complex SQL, query optimization, and performance tuning in cloud data warehouses.

• Strong Python Skills: Proven experience building production-quality data automation, validation, and orchestration logic.

• Modern Data Stack: Extensive hands-on experience with Snowflake, dbt, and cloud-native data architectures.

• Orchestration: Strong experience with Airflow or equivalent scheduling and workflow management tools.

• Data Quality & Governance: Demonstrated ownership of audit-ready, incentive- or finance-critical datasets.

• Technical Authority: Recognized as a go-to expert for complex data engineering problems.

• Ownership Mindset: Drives initiatives end-to-end, from design through production and ongoing optimization.

• Systems Thinking: Able to reason across data, pipelines, tooling, and business processes holistically.

• Learning & Innovation: Actively adopts modern engineering practices and AI-assisted workflows.

• Collaboration & Influence: Effectively partners across engineering, analytics, finance, and business teams without formal authority.

• Execution Under Ambiguity: Thrives in fast-moving environments with evolving requirements and high business impact.

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.