Sr. Manager, Data Engineering

United StatesRemoteFull-timesenior

EngineeringData Engineering

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Quick Summary

Overview

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.

Technical Tools

EngineeringData Engineering

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.

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.

Requirements

~2 min read

3+ years of experience leading or managing data engineering teams, combined with 5+ years of hands-on data engineering experience in cloud environments.
Deep hands-on experience with GCP technologies, particularly BigQuery, Cloud Composer/Apache Airflow, dbt, Python, and SQL.
Working knowledge of related cloud technologies such as Dataflow, Pub/Sub, Cloud Storage, Looker, and Cloud IAM, as well as Infrastructure as Code for automating IAM and data policy controls.
Expert-level knowledge of data modeling and architecture frameworks, with the ability to balance technical design decisions against cost, business requirements, scalability, and future growth.
Broad understanding of data visualization, data governance, artificial intelligence, and machine learning disciplines.
Strong project execution and delivery management skills, including the ability to establish timelines, track progress, identify issues early, and adjust priorities effectively.
Excellent communication and stakeholder management skills, with the ability to explain complex technical concepts to non-technical audiences and collaborate effectively across time zones.
Demonstrated hands-on use of AI tools such as Copilot, Claude, ChatGPT, or comparable solutions in engineering workflows, with a practical understanding of their impact on productivity, code quality, and development practices.
Experience driving AI adoption among engineering teams or the ability to lead organizational adoption through coaching, standards, and effective change management.
Strong sense of technical accountability, ensuring AI-assisted outputs are appropriately reviewed, tested, and owned by engineers.
Comfortable operating in ambiguous and rapidly changing environments, making informed decisions with incomplete information and prioritizing work based on business value.
Experience building foundational data platforms and capabilities rather than exclusively maintaining mature environments.
Proactive, curious, adaptable, and results-oriented approach with strong business acumen and a commitment to continuous improvement.
Candidates from diverse professional and educational backgrounds are encouraged to apply, including those without a traditional technology degree or career path.