Data Engineer Principal (Enterprise Data Group)

Description

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 architects, builds, and operates the foundational data platform and enterprise data lake that powers operational visibility, real-time analytics, and business-critical decision systems across the global logistics network.

The Data Engineering Principal serves as the senior technical authority and architect across enterprise data engineering initiatives. Operating at the intersection of architecture, transformation, and deployment, this individual defines the technical blueprints for petabyte-scale batch and streaming pipelines, modern lakehouse structures, and governed data products. While partnering with engineering managers on technical roadmap execution, the Principal leads through hands-on technical guidance, driving best-in-class engineering standards, conducting rigorous code and design reviews, solving the most complex scalability hurdles, and mentoring engineering talent across multi-disciplinary squads.

1. Strategic Architecture & Lakehouse Design

• Enterprise Architecture Blueprints: Translates business analytics requirements, reporting demands, and strategic operational initiatives into scalable, cloud-native data architecture blueprints and reusable technical reference models.
• Lakehouse & Medallion Strategy: Leads the technical design and standardization of enterprise data lakehouse architectures (medallion model: Raw/Bronze, Conformed/Silver, and Curated/Gold), establishing optimized dimensional schemas, analytical aggregates, and unified semantic layers.
• Pattern Codification: Champions the design, building, and governance of reusable data engineering assets, frameworks, and deployment templates to accelerate delivery and ensure consistency across distributed teams.
• Service-Oriented Integration: Establishes and enforces architectural standards for decoupled publish/subscribe patterns, event-driven architectures, and API-first data accessibility.

2. Big Data Pipeline Design, Ingestion & Transformation

• High-Throughput Ingestion Frameworks: Architect and oversee the implementation of resilient, high-throughput ingestion pipelines capturing data across disparate enterprise sources—including legacy databases, ERP/TMS operational applications, mainframe transaction systems, and message queues (e.g., Apache Kafka, JMS relays).
• Distributed Transformation Engines: Designs and optimizes scalable, fault-tolerant transformation layers utilizing distributed computing engines (e.g.,
• Apache Spark/PySpark, Dataproc) and modern analytics engineering frameworks (e.g., dbt, modular SQL).
• Workflow Orchestration & Resilience: Establishes standards for enterprise DAG pipeline orchestration (e.g., Apache Airflow, Cloud Composer), ensuring deterministic job scheduling, automated backfills, lineage capture, and robust disaster recovery capabilities.
• Complex Data Problem Resolution: Independently designs and deploys complex parsers, validators, transformers, and correlators to resolve multi-domain data conformance challenges and bridge laptop-scale models to cluster-scale execution.

3. Data Governance, Quality & Security Architecture

• Automated Data Quality & Observability: Directs the implementation of automated data validation, contract enforcement at the ingestion boundary, anomaly detection, and schema evolution controls (e.g., Great Expectations, data contracts) to guarantee enterprise data integrity.
• Metadata & Lineage Management: Embeds automated metadata registration, catalog discovery, and end-to-end data lineage tracking into pipeline lifecycles, ensuring auditability and traceability from raw ingestion through consumption.
• Access Governance & Security Controls: Collaborates with Information Security to architect and enforce enterprise data security standards, including column/row-level security, data masking/tokenization, encryption in transit/at rest, and granular RBAC/ABAC models.
• SLA & Resource Cost Optimization: Audits and establishes optimization patterns for storage, cluster compute resources, partitioning, and clustering strategies to enforce performance SLAs while maximizing cloud cost efficiencies.

4. Deployment, DevOps & Operational Reliability

• Modern DataOps & CI/CD Standards: Champions modern software engineering and DataOps discipline within the data engineering practice, including automated testing suites, continuous integration and continuous deployment (CI/CD) pipelines, and Infrastructure-as-Code (IaC).
• Production Deployment Oversight: Leads release reviews, capacity planning, and deployment governance for critical, high-volume data pipelines transitioning from development to 24/7 production environments.
• Performance Tuning & Troubleshooting: Serves as the premier escalation engineer to diagnose and resolve complex distributed computing bottlenecks, performance regressions, network latency, and memory/serialization failures in production.

5. Technical Leadership, Mentorship & Cross-Functional Influence

• Engineering Mentorship & Talent Upskilling: Actively coaches and mentors junior, mid-level, and senior data engineers; fosters an engineering culture grounded in craftsmanship, technical precision, and continuous learning.
• Architectural & Code Reviews: Conducts deep, constructive architectural assessments and pull request (PR) code reviews to maintain the highest standard of software and data engineering hygiene.
• Cross-Functional Bridge: Acts as a strategic technical liaison between Engineering Management, Enterprise Architecture, Data Science, Business Intelligence, Product Management, and Cyber Security to negotiate technical trade-offs and drive platform adoption.
• Technology Radar & Innovation: Evaluates emerging tools, storage formats, and frameworks across the broader technology ecosystem to ensure FedEx maintains modern, competitive capabilities for large-scale data processing.

Preferred Knowledge, Skills, and Abilities

1. Distributed Computing, Ingestion & Messaging Systems

• Distributed Processing Engines: Deep expertise in distributed data processing frameworks, primarily Apache Spark (PySpark, Spark SQL, Scala) on managed cloud clusters (e.g., Google Cloud Dataproc, Databricks).
• Streaming & Event-Driven Ingestion: Proven mastery in designing and maintaining event-driven, real-time data streaming and message brokering solutions utilizing Apache Kafka, Google Cloud Pub/Sub, Kafka Connect, or enterprise JMS relays.
• Change Data Capture (CDC): Practical proficiency in implementing robust CDC pipelines to stream updates from transactional databases (Oracle, PostgreSQL, SQL Server, mainframe DB2) into data lakehouse environments.

2. Transformation, Modeling & Enterprise Lakehouse Architecture

• Modern Transformation Workflows: Expert-level SQL and deep hands-on proficiency with modern analytics engineering tools such as dbt (data build tool) for modular, version-controlled transformations.
• Lakehouse Formats & Cloud Storage: Extensive experience designing storage layouts utilizing open columnar and table formats (Apache Iceberg, Delta Lake, Parquet) across scalable cloud object storage (Google Cloud Storage / AWS S3).
• Data Modeling & Analytics Engineering: Mastery of dimensional modeling (Kimball, Inmon, Data Vault, Medallion Architecture), conformed dimensions, metric stores, and analytical marts configured for low-latency BI and high-concurrency querying.
• Pipeline Orchestration: Deep proficiency in authoring, scaling, and operationalizing complex directed acyclic graphs (DAGs) using orchestration frameworks such as Apache Airflow or Google Cloud Composer.

3. Cloud Platforms, Governance & Observability

• Cloud Data Warehousing: Advanced proficiency in designing, tuning, and querying high-performance analytical warehouses (e.g., Google BigQuery, Snowflake), including clustering, partitioning, and compute slot management.
• Data Observability & Contract Enforcement: Demonstrated ability to build automated data quality frameworks, implement data contract architectures, and set up telemetry for pipeline drift and anomalies.
• Data Governance & Compliance: Solid working knowledge of enterprise metadata management, data catalog integration, data lineage tracking, and regulatory security standards (RBAC, ABAC, data tokenization/masking).
• Cloud Ecosystems: Primary fluency with modern cloud data platforms (e.g., Google Cloud Platform); multi-cloud awareness (including Azure or AWS data services) is highly desirable.

4. Software Engineering Discipline & CI/CD

• Software Craftsmanship: High standard of software engineering practices, including object-oriented/functional programming in Python, Java, or Scala.
• DevOps / DataOps: Hands-on experience with version control (Git), containerization (Docker), CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins), and infrastructure automation tools (Terraform).
• System Testing & Validation: Strong discipline in writing unit tests, automated integration tests, regression suites, and schema validation scripts for pipeline builds.

5. Technical Leadership & Collaborative Communication

• Strategic Technical Communication: Proven ability to translate complex architectural trade-offs, system constraints, and engineering decisions into clear language for business executives, product managers, and cross-functional teams.
• Architecture Advisory & Mentorship: Demonstrated capability to guide and upskill engineering teams, establish technical roadmaps, lead PR reviews, and unblock difficult engineering challenges.
• Pragmatic Problem Solving: Strong bias for engineering pragmatism—balancing theoretical system ideals with delivery timelines, operational stability, and infrastructure cost.

Minimum Education

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. Directly related PhD preferred.

Minimum Experience

Seven (7) 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 include design, development and implementation of highly complex systems and data pipelines. Extensive knowledge in Information Systems includes design, development and implementation of large batch or online transaction-based systems. Strong understanding of the transportation industry, competitors, and evolving technologies. Experience providing expert leadership in a general planning or consulting setting. Experience as a leader or a senior member of multi-functional project teams. Excellent interpersonal skills and the ability to communicate effectively to senior audiences. A related advanced degree may offset related experience requirements.

Business Summary

The Data Engineer Principal plays a pivotal role within FEC and provides dynamic leadership at the enterprise level. S/he is focused on fundamentally creating and driving engineering innovation within FEC, helping to define and build the FEC organization and overseeing the delivery of key business initiatives. S/he acts as a “universal translator” between IT, business, software engineers and data scientists, collaborating with these multi-disciplinary teams. The Data Engineer Principal will champion the creation and adherence of technical standards for data engineering, including the selection and refinement of foundational technical components. S/he will work on those aspects of the FEC platform that govern the ingestion, transformation, and pipelining of data assets, both to end users within FedEx and into data products and services that may be externally facing. Day-to-day, s/he will be deeply involved in code reviews, large scale deployments and general oversight. S/he will also mentor junior engineers to support the continued up-skilling of the Data Engineering team.

Preferred Qualifications:  

Pay Transparency:   

Pay: Plano and Pittsburgh: $11,766.26/mo to $15,884.45/mo Memphis: $11,177.95/mo to $15,090.23/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.

Applicants have rights under Federal Employment Laws:

• Know Your Rights
• Pay Transparency
• Family and Medical Leave Act (FMLA)
• Employee Polygraph Protection Act

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