Data Engineering Manager, Commercial US

Main Responsabilities

Lead, mentor, and develop a team of data engineers while fostering strong engineering practices, accountability, collaboration, and continuous improvement

Design, build, deploy, and support scalable data pipelines and data products that enable analytics, AI/ML, and commercial business use cases

Lead discovery, solution design, and technical planning discussions with business, analytics, AI, and technology teams

Manage team delivery, priorities, capacity planning, and execution across multiple concurrent data engineering initiatives

Design, develop, test, and optimize scalable data engineering solutions and reusable data assets that support analytics, AI/ML, and business-critical workflows across global platforms

Partner with technical and non-technical stakeholders to clarify ambiguous business needs, shape solution approaches, and translate requirements into scalable data engineering solutions

Provide architectural and technical leadership across data pipeline orchestration, distributed processing, cloud-native platforms, and data integration patterns

Drive operational excellence across production data assets, including monitoring, troubleshooting, incident response, release management, and continuous improvement

Identify opportunities to automate, simplify, standardize, and optimize data engineering processes, reusable assets, and platform capabilities

Collaborate within cross-functional agile teams and partner with internal and external stakeholders to deliver high-quality data engineering solutions

Contribute to and evolve data engineering standards, best practices, and community knowledge sharing across the organization

Stay current with emerging technologies, industry trends, and modern data engineering practices to continuously improve platform capabilities and engineering effectiveness

About You

Qualifications

6+ years of experience in data engineering, analytics engineering, or data platform development, including 2+ years leading or managing engineering teams

Demonstrated experience designing, building, and operating scalable data pipelines, data platforms, and distributed processing solutions using technologies such as Spark, Kafka, Snowflake, Hadoop, or similar

Strong experience with cloud-native data engineering and modern ETL/ELT solutions, preferably within Snowflake / AWS-based environments; Informatica/IICS experience preferred

Advanced SQL and data modeling skills, with working knowledge of Python and scripting languages; Scala or Java is a plus

Experience with batch, near real-time, and streaming data architectures, as well as modern data warehouse, lake, and lakehouse concepts including data mesh principles

Strong understanding of data architecture, scalability, reliability, performance optimization, and operational support for enterprise-grade data platforms

Demonstrated ability to work with technical and non-technical stakeholders to navigate ambiguity, identify underlying business needs, and translate them into scalable technical solutions and execution plans

Strong communication, facilitation, and stakeholder management skills, with the ability to influence decisions and communicate complex technical concepts to diverse audiences

Experience partnering with cross-functional teams including analytics, AI/ML, product, infrastructure, security, governance, and business stakeholders

Experience operating in agile delivery environments with strong understanding of software engineering practices, CI/CD, release management, testing, and operational support

Experience leading engineering teams through delivery execution, prioritization, mentoring, performance management, and continuous improvement initiatives

Bachelor’s or Master’s degree in Computer Science, Engineering, STEM, Business, or a related field, or equivalent practical experience