Manager, Engineering (Enterprise Data Group)

Domicile Information: This is a hybrid position located in Memphis, TN; Plano, TX; or Pittsburgh, PA. Candidates must live within 50 miles of the campus location. Employees will be required to work at the FedEx campus location several times per week. Essential Functions About the Team - The Enterprise Data Group at FedEx is responsible for architecting, building, and operating the foundational data platforms and enterprise data lake that power analytics, operational visibility, and business decision-making across the global FedEx logistics network. Our mission is to deliver governed, high-throughput, and clean data assets at petabyte scale across all operational domains. The Manager of Data Engineering provides strategic technical leadership, operational oversight, and people management for an engineering organization responsible for foundational data platforms. This role balances long-term architectural vision with day-to-day operational excellence. In addition to guiding enterprise data lakehouse architecture and modern ingestion patterns, this leader oversees 24/7 production support operations, manages vendor contracts and software licensing, directs annual budget forecasting and cloud spend (FinOps), and fosters a high-performing engineering culture. 1. Strategic Platform & Pipeline Architecture 1. Enterprise Data Architecture: Translates enterprise business priorities, analytical demands, and operational initiatives into robust data architecture blueprints and long-term capability roadmaps. 2. Modern Ingestion & Processing Architecture: Directs the architectural design and evolution of scalable, decoupled data ingestion and processing frameworks-incorporating both distributed batch and real-time streaming paradigms across modern cloud platforms (e.g., Google Cloud Platform / BigQuery, Dataproc, Cloud Storage). 3. Lakehouse & Modeling Strategy: Champions standardized enterprise data lakehouse architectures (e.g., medallion layers: Raw/Bronze, Conformed/Silver, Curated/Gold), ensuring conformed dimensions, metric stores, and domain models optimize analytical query performance and storage efficiency. 4. Service-Oriented Integration: Enforces alignment with enterprise architecture standards, API-first strategies, decoupled event-driven systems (e.g., Kafka, JMS relays), and data mesh/conformance principles across diverse source systems. 2. Production Support & Operational Reliability 1. 24/7 Operational Ownership: Owns the run-the-engine production operations for critical data pipelines and platforms, ensuring high availability, system resilience, and strict adherence to enterprise service level agreements (SLAs). 2. Incident Management & Escalation: Leads tier-2 and tier-3 incident response for data outages, pipeline failures, and latency bottlenecks; establishes robust on-call rotations, disaster recovery plans, and operational runbooks. 3. Root Cause Analysis (RCA): Facilitates thorough post-incident reviews, driving corrective and preventive actions to eliminate systemic technical debt and recurring platform issues. 4. Operational Monitoring & Observability: Directs the implementation of comprehensive telemetry, automated alerting, pipeline health dashboards, and synthetic monitoring across the end-to-end data estate. 3. Budget Projection, FinOps & Contract Management 1. Budget Planning & Forecasting: Develops annual operating and capital budget projections for the data engineering organization, forecasting compute, storage, software, and personnel expenditures. 2. Cloud Cost Governance (FinOps): Establishes financial governance and cost-allocation mechanisms across cloud data assets (e.g., GCP BigQuery, compute clusters, storage tiers); actively monitors consumption metrics, identifies cost optimization opportunities, and ensures predictable spend. 3. Vendor & Contract Management: Collaborates with Sourcing, IT Procurement, and Legal to evalua e, negotiate, and manage third-party software licenses, optimization solvers, SaaS platforms, and managed service provider (MSP) statements of work (SOWs). 4. Vendor Performance & Renewal Tracking: Audits software license usage, oversees vendor SLA compliance, tracks renewal timelines, and aligns vendor commitments with internal project delivery schedules. 4. Data Governance, Quality & Compliance 1. Data Quality Frameworks: Directs the deployment of automated data validation, schema enforcement, data contract standards, and anomaly detection at ingestion boundaries. 2. Metadata & Lineage Governance: Ensures end-to-end data lineage, data cataloging, and metadata registration are systematically maintained for compliance, discoverability, and regulatory audits. 3. Security & Access Control: Enforces enterprise cybersecurity baselines, encryption standards, role-based access control (RBAC/ABAC), and data privacy/masking rules across all managed repositories. 5. People Leadership & Delivery Execution 1. Talent Development: Recruits, develops, and retains high-caliber Data Engineers and Leads; builds structured career pathways, promotes engineering best practices, and conducts continuous performance coaching. 2. Agile Program Delivery: Oversees project portfolio execution using Agile methodologies, managing backlogs, balancing new capability development against operational maintenance, and setting realistic delivery milestones. 3. Stakeholder Alignment: Serves as the primary conduit between technical teams and cross-functional leadership (Business Intelligence, Data Science, Core Operations, and Enterprise Architecture), managing expectations and articulating technical trade-offs in business terms. Preferred Knowledge, Skills, and Abilities Architectural & Platform Competencies 1. Enterprise Architecture Acumen: In-depth understanding of enterprise data architectures, medallion lakehouse patterns, dimensional modeling, and decoupled event-driven topologies. 2. Cloud Data Ecosystems: Working architectural knowledge of enterprise cloud data platforms (e.g., Google Cloud Platform, BigQuery, Dataproc, Cloud Storage, or equivalent AWS/Azure technologies). 3. Integration Patterns: Familiarity with modern data ingestion mechanics, including Change Data Capture (CDC), streaming message brokers (Kafka, Pub/Sub), message relays, and distributed batch processing (Spark). 4. Data Governance & Observability: Strong conceptual understanding of data contracts, metadata cataloging, data lineage, and automated data quality frameworks (e.g., Great Expectations). Operational & Financial Management Competencies Production Operations Leadership: Demonstrated experience managing production support for mission-critical, high-throughput systems, including incident escalation, on-call management, and RCA processes. Cl