Data Engineer Principal

Domicile Information: This is a hybrid position in Plano, TX. Candidates residing within 50 miles of a FedEx campus will be required to work on-site at a FedEx location several times per week. 

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.

Essential Functions

• Define and evangelize the enterprise data platform architecture: lakehouse/warehouse patterns, ingestion/topology, storage tiers, data meshes, service boundaries, and governance guardrails.
• Design and build scalable batch and streaming data pipelines: CDC, bulk ingestion, event-driven streams, and real-time processing using technologies such as Kafka/Pub/Sub, Spark/Dataproc, Dataflow/Beam, Flink, or similar.
• Lead data modeling and schema strategy: canonical models, dimensional models, normalized/denormalized schemas, partitioning, clustering, and time-series best practices to maximize performance and cost-efficiency.
• Architect and implement ETL/ELT frameworks and orchestration: Airflow/Composer, Prefect, Dagster, Glue workflows, and robust dependency, retry, backfill and SLA handling.
• Design data storage and query platforms at scale: object storage, columnar warehouses (BigQuery/Redshift/Snowflake/Synapse), OLAP engines, and caching layers; define retention, compaction, and lifecycle policies.
• Build and maintain metadata, catalog, lineage and discovery capabilities: integrate Data Catalog/Atlas/Alation and automated lineage capture to support governance and self-serve analytics.
• Implement data quality, validation and observability: schema checks, unit tests, Great Expectations/Deequ checks, anomaly detection, SLIs/SLOs for data freshness, completeness and accuracy, and end-to-end monitoring and alerting.
• Drive security and compliance controls for data: fine-grained access controls (IAM, RBAC, ABAC), encryption (KMS/CMEK), tokenization/de-identification, PII discovery, VPC/private connectivity for data flows, and audit logging.
• Build reproducible infrastructure and platform modules for data workloads: Terraform/CloudFormation/Deployment Manager, container images, cluster templates, autoscaling, and policy-as-code for platform consistency.
• Optimize cost and performance of data workloads: query performance tuning, storage format choices (Parquet/ORC/Avro), compaction, caching, CUD or reserved capacity planning, and FinOps for analytics.
• Lead migrations and modernization efforts: lift-and-shift and re-architecture of on-prem or cross-cloud ETL and warehouses to modern cloud data platforms; own cutover, rollback, validation of RTO/RPO and performance targets.
• Enable ML/BI consumption: design feature stores, data marts, model inputs pipelines, and publishing patterns to support analytics, BI, and ML lifecycle teams.
• Establish CI/CD and dataops practices: testing (unit/integration/regression), artifact promotion, data pipeline deployments, schema evolution strategies, and GitOps for data infrastructure and pipelines.
• Coach and mentor data engineering teams and stakeholders: produce runbooks, design reviews, coding standards, migration playbooks, and transfer operational ownership to platform or application teams

Preferred Knowledge, Skills, and Abilities

• 8+ years of data engineering experience; 4+ years designing and operating large-scale cloud data platforms.
• Hands-on delivery of at least one end-to-end data platform migration or large-scale modernization (on-prem to cloud or cloud-to-cloud).
• Strong SQL skills and advanced programming experience in Python, Scala or Java; production experience with Spark, Beam, Flink or equivalent.
• Deep experience with at least one cloud data ecosystem (Google BigQuery/Dataflow) and associated managed services.
• Proven experience with streaming platforms (Kafka/Pub/Sub) and CDC tools (Debezium/Datastream/GoldenGate).
• Experience with orchestration and workflow tools (Airflow) and data testing/validation frameworks (Great Expectations, Deequ).
• Familiarity with data governance, metadata/catalog tools (Atlan) and regulatory controls (GDPR, HIPAA, PCI).
• Good to have certifications: Google Professional Data Engineer, AWS Certified Data Analytics, SnowPro Core, or comparable cloud/data certifications.
• Experience with infrastructure-as-code (Terraform), containerization/Kubernetes for data workloads, and ML feature-store concepts (Feast or equivalent) is a plus.

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.

Preferred Qualifications: 

Pay Transparency:  This compensation range is provided as a reasonable estimate of the current starting salary range for this role across all potential locations. If this opportunity includes multiple job levels, the range is a reasonable estimate of the current  starting salary for the lowest level to the current starting salary of the highest level. Actual starting pay would be determined by experience relative to the job, market level, pay at the location for this job and other job-related factors  permitted by law. An employee may be eligible for additional pay, premiums, or bonus potential. The Company offers eligible employees health, vision and dental insurance, retirement, and tuition reimbursement. 

Pay:  Plano Pay Range: $11,766.26/monthly-$15,884.45/monthly 

Additional Details: 

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 View email address on careers.fedex.com .

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