Sr. Manager, Data Engineering

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. Manager, Data Engineering based in United States. This is a high-impact leadership role focused on building and scaling a modern data engineering foundation from the ground up. You will lead a growing global team while remaining hands-on with architecture, engineering standards, cloud data platforms, and delivery. The role combines people leadership with deep technical ownership across data pipelines, integration, DataOps, governance, analytics, and AI readiness. You will make foundational architectural decisions and establish the standards that future data capabilities will depend on. The environment is fast-moving and collaborative, with evolving priorities and a strong emphasis on experimentation, continuous improvement, and measurable business outcomes. This opportunity is particularly suited to a proactive data leader who is energized by ambiguity and experienced in turning emerging technologies into practical engineering capabilities. \n Accountabilities: - Lead, mentor, and develop a growing global team of data engineers, including talent reviews, career development, hiring, onboarding, and fostering a culture of continuous learning. - Own team planning, estimation, prioritization, delivery tracking, intake coordination, stakeholder communication, and roadmap planning in alignment with organizational priorities. - Establish and enforce engineering and architecture standards covering code quality, testing, documentation, production readiness, security, and maintainability. - Design, build, operationalize, and oversee scalable data pipelines connecting enterprise applications, internal services, and third-party APIs.

- Lead production data engineering across ingestion, transformation, modeling, and consumption using technologies such as GCP, BigQuery, Python, SQL, dbt, Apache Airflow, and related integration tools. - Design and oversee data models using medallion architecture principles while maintaining high standards for data validation, profiling, reconciliation, and quality. - Drive AI adoption across engineering workflows by championing practical use of AI tools for development, code reviews, documentation, debugging, and productivity. - Establish quality standards for AI-assisted engineering work, ensuring appropriate human review, testing, accountability, and technical judgment before production release. - Measure the impact of AI adoption on delivery speed, quality, and engineering experience, using evidence to guide future investments and practices. - Build reliable, well-modeled, well-documented, and AI-ready data assets that can be consumed effectively by analytical systems, agents, and machine learning applications. - Define and manage SLAs for critical datasets, including expectations for freshness, completeness, and accuracy. - Develop monitoring and alerting for data pipelines, orchestration, and platform health while supporting incident response, production operations, and on-call practices. - Mature CI/CD, environment management, and release processes for data and analytics code. - Translate business objectives into scalable data, analytics, automation, and AI solutions in partnership with business and technology stakeholders. - Promote data democratization and self-service analytics while evaluating tools and end-to-end solutions across data engineering, analytics, machine learning, and data governance. - Partner with data governance teams to ensure data products are properly documented, owned, trustworthy, and designed with appropriate controls. - Apply systems thinking to identify underlying business and technical challenges, sequence work effectively, and deliver measurable value early.