Sr. Manager, Data Engineering
Indexed description
This is a high-impact opportunity for a technically grounded, people-first leader who thrives in a fast-paced environment and is energized by building scalable systems during a period of significant growth, including M&A integration and cloud transformation.
Responsibilities
- Leadership & Strategy
- Define and drive the enterprise data engineering roadmap, aligned to business and technology goals
- Build, lead, and mentor a multi-disciplinary team of data engineers (10-15) and product owner(s) (1-2)
- Partner with the CIO, Analytics, AI, and business leadership to prioritize and deliver data capabilities
- Establish engineering standards, best practices, and an operational excellence framework across the data organization
- Champion a culture of data quality, engineering rigor, and continuous improvement
- Platform & Architecture
- Architect and oversee scalable data pipelines, data lakes, warehouses, and real-time streaming infrastructure
- Lead cloud-native data platform strategy and adoption across Azure, AWS, or GCP environments
- Own the selection, implementation, and lifecycle management of data engineering tools and platforms
- Ensure data platform reliability, performance, and cost efficiency at scale
- Partner with Platform Development Architects and leadership on integration patterns across key enterprise systems, including ERP, ServiceNow, and supply chain platforms
- Data Governance & Quality
- Own and evolve the enterprise data governance framework, including data lineage, cataloging, and access controls
- Define and enforce data quality standards, measurement, and remediation processes
- Partner with Security and Legal to ensure compliance with data privacy regulations and policies
- In partnership with the Director of Development and AI, drive adoption of clean, governed, AI-ready data as a core organizational asset.
- Delivery & Operations
- Manage team delivery across multiple concurrent programs, including integration workstreams from M&A activity
- Lead data engineering support for AI/ML platform enablement
- Oversee vendor relationships, contracts, and budget for data infrastructure and tooling
- Establish SLAs, incident management, and monitoring for data platform operations
- 10+ years of progressive experience in data engineering, data architecture, or a related technical discipline
- 5+ years of people leadership experience, including managing and developing senior engineers and managers; Communication is a critical facet for this role with an ability to manage up, to the side or down.
- Proven track record of architecting and delivering enterprise-scale data platforms in production
- Deep expertise in modern data stack tooling (e.g., Azure SQL, Azure Fabric, Kafka, Snowflake, Databricks, or equivalents)
- Strong cloud data platform experience on Azure, AWS, or GCP
- Demonstrated experience in data governance programs and data quality frameworks
- Ability to translate complex business requirements into pragmatic technical strategy and roadmaps
- Strong executive communication skills; comfortable presenting to C-suite and board-level audiences
- Experience operating in dynamic, growth-stage environments, including M&A integration preferred
- Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field; Master’s preferred
- Experience in IT services, managed services, or enterprise technology industries
- Familiarity with ERP, ServiceNow, or supply chain data ecosystems
- Experience leading data strategy and integration through M&A events
- Exposure to data mesh, data product, or federated governance models
- Relevant certifications in cloud data platforms (e.g., AWS Certified Data Analytics, Azure Data Engineer Associate)
- A strategic mindset paired with a bias for action and hands-on delivery
- Passion for building reliable, scalable, and well-governed data systems
- A collaborative and inclusive leadership style with the ability to influence across levels
- Intellectual curiosity and a continuous improvement mindset
- High standards for data quality and a commitment to engineering excellence
- 15%
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