FedEx
From FedEx's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
This role balances long-term architectural vision with day-to-day operational excellence.
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.
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
Enterprise Data Architecture: Translates enterprise business priorities, analytical demands, and operational initiatives into robust data architecture blueprints and long-term capability roadmaps.
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).
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.
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
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).
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.
Root Cause Analysis (RCA): Facilitates thorough post-incident reviews, driving corrective and preventive actions to eliminate systemic technical debt and recurring platform issues.
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
Budget Planning & Forecasting: Develops annual operating and capital budget projections for the data engineering organization, forecasting compute, storage, software, and personnel expenditures.
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.
Vendor & Contract Management: Collaborates with Sourcing, IT Procurement, and Legal to evaluate, negotiate, and manage third-party software licenses, optimization solvers, SaaS platforms, and managed service provider (MSP) statements of work (SOWs).
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
Data Quality Frameworks: Directs the deployment of automated data validation, schema enforcement, data contract standards, and anomaly detection at ingestion boundaries.
Metadata & Lineage Governance: Ensures end-to-end data lineage, data cataloging, and metadata registration are systematically maintained for compliance, discoverability, and regulatory audits.
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
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.
Agile Program Delivery: Oversees project portfolio execution using Agile methodologies, managing backlogs, balancing new capability development against operational maintenance, and setting realistic delivery milestones.
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.
Architectural & Platform Competencies
Enterprise Architecture Acumen: In-depth understanding of enterprise data architectures, medallion lakehouse patterns, dimensional modeling, and decoupled event-driven topologies.
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).
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).
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.
Cloud Financial Management (FinOps): Proven experience forecasting cloud infrastructure spend, tracking resource utilization, and driving compute/storage cost-efficiency initiatives.
Contract & Vendor Management: Experience auditing software licensing models (e.g., core-based, consumption-based, user-tiered), negotiating vendor renewals, and managing vendor deliverable timelines.
Service Level Management: Ability to define, measure, and uphold tight operational SLAs and SLOs for data freshness, availability, and pipeline performance.
Leadership & Professional Competencies
Executive & Technical Communication: Exceptional capability to articulate complex architectural concepts, financial trade-offs, and operational risks to executive stakeholders and technical teams alike.
People Leadership & Coaching: Proven track record of leading, mentoring, and scaling engineering teams while cultivating an inclusive culture of accountability and continuous improvement.
Strategic Prioritization: Strong analytical and organizational skills to manage competing operational emergencies and long-term strategic roadmaps simultaneously.
Bachelor's Degree in Information Systems, Computer Science, or a quantitative discipline such as Mathematics or Engineering and/or equivalent formal training or work experience.
Five to eight (5-8) years equivalent work experience in measurement and analysis, quantitative business problem solving, simulation development and/or predictive analytics. Extensive knowledge in data engineering and machine learning frameworks including design, development and implementation of highly complex systems and data pipelines. Extensive knowledge in Information Systems including design, development and implementation of large batch or online transaction-based systems. Strong understanding of the transportation industry, competitors, and evolving technologies. Experience providing leadership in a general planning or consulting setting. Experience as a leader or a senior member of multi-function project teams. Strong oral and written communication skills. A related advanced degree may offset the related experience requirements.
Business Summary
Takes ownership and responsibility for the support the design, build, test and maintain data pipelines at big data scale. Leads a team responsible for modeling and development to support operations initiatives, strategic programs and new products and solutions.
Strong background and experience in the use of advanced descriptive, diagnostic, predictive, prescriptive and ensemble modeling, advanced statistical techniques, and complex mathematical tools. Uses expertise to lead a team that initiates and delivers projects and develops end-to-end solutions that drive business results, including proofs-of-value for new business problems and production-ready solutions for operations and customer-facing products. Understands and ensures development of solutions supporting the movement of data and information assets following API-First / Service-Oriented Architecture principles. Advances Dataworks’ broad capabilities to use and deploy cutting edge data science and machine learning tools in Dataworks projects, platforms and products. Provides expert consultation and thought leadership to senior management. Mentors other team members to drive results and effectively collaborates with cross-functional teams to deliver business goals.
Pay: Plano and Pittsburgh: $10,231.53/mo to $13,812.57/mo ; Memphis: $9,719.96/mo to $13,121.94/mo
Additional Details: Application Criteria: Upload current copy of Resume (Microsoft Word or PDF format only) and answer job screening questionnaire.
For details on our comprehensive benefits, click here.
Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability.
Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact recruitmentsupport@fedex.com.
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Family and Medical Leave Act (FMLA)
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