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.
Experience participating in at least one GCP-focused data platform migration or modernization as an advisor, reviewer, or core contributor.
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.
The Data Engineer Lead plays a pivotal role, focused on creating and driving engineering innovation, helping define and build and facilitate 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 Lead will contribute to the creation of and adherence to technical standards for data engineering, including the selection and refinements of foundational technical components. S/he will work on those aspects of the 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 and large-scale deployments. Under minimal supervision, supports the design, build, test and maintain data pipelines at big data scale. Assists with updating data from multiple data sources. Other functionalities under close supervision is to work on batch processing of collected data and match its format to the stored data, make sure that the data is ready to be processed and analyzed. Assists with keeping the ecosystem and the pipeline optimized and efficient, troubleshooting standard performance, data related problems and provide L3 support. Independently implements parsers, validators, transformers and correlators to reformat, update and enhance the data. Provides recommendations to highly complex problems. Provides guidance to those in less senior positions.
Provide practical, hands-off guidance on GCP data platform and pipeline architecture: recommend patterns for ingestion, storage, modeling, and governance that match the organization’s maturity and goals.
Review and critique proposed GCP designs and implementation plans for scalability, reliability, security, and cost; surface risks and pragmatic mitigation options.
Recommend GCP-specific ingestion and processing patterns: Pub/Sub -> Dataflow (Beam) streaming, Dataflow batch/ETL, Dataproc/Spark jobs, Transfer Service/Storage for bulk loads, and Datastream for CDC; suggest tradeoffs between managed services and containerized workloads.
Advise on BigQuery design and optimization: schema strategies, partitioning/clustering, materialized views, slots/reservations, BI Engine, cost-control patterns, and query performance best practices.
Recommend storage and stateful service patterns on GCP: Cloud Storage lifecycle/tiering, Bigtable/Firestore use-cases, Cloud SQL/AlloyDB considerations for analytics use, and caching strategies.
Propose metadata, catalog and lineage approaches using Data Catalog, Pub/Sub/Dataflow instrumentation, and integration with governance tools; define metadata and discovery expectations.
Advise on data quality and observability for GCP pipelines: validation checkpoints in Dataflow, Great Expectations or similar, Cloud Monitoring/Logging instrumentation, and data-SLO/SLI recommendations.
Define security and compliance guardrails on GCP at an architecture level: BigQuery IAM/data access models, Authorized Views, VPC Service Controls, Workload Identity, Cloud KMS/CMEK, Secret Manager, and Data Loss Prevention (DLP) patterns for PII.
Recommend CI/CD and deployment patterns for data workloads on GCP: Dataflow templates, Composer/Airflow orchestration, artifact promotion strategies, and IaC (Terraform) governance for data platform resources.
Provide cost/governance guidance specific to GCP analytics: labeling, budgets, BigQuery cost controls, committed use/flat-rate considerations, and high-level FinOps guardrails.
Produce concise guidance artifacts for engineering teams: GCP architecture checklists, design-review templates, migration playbooks for GCP services, runbook outlines, and cutover/validation checklists.
Mentor and upskill engineers on GCP data best practices via reviews, brown-bags, and targeted coaching; enable teams to adopt patterns without owning delivery.
Support migration and modernization efforts in an advisory capacity for GCP moves: contribute to risk assessments, cutover criteria, performance/RTO-RPO validation approaches, and rollback plans.
5+ years in data engineering or related roles with experience advising or influencing technical direction; 2+ years working with GCP data services in a practical or architectural capacity.
Hands-on or review experience with core GCP data services: BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Storage, Dataproc/Dataproc Serverless, Cloud Composer (Airflow), Datastream/Transfer Service, and Data Catalog.
Practical knowledge of BigQuery performance and cost patterns (partitioning, clustering, materialized views, reservations/slots) and common anti-patterns.
Familiarity with GCP security and compliance features relevant to data: IAM for datasets, Authorized Views, VPC Service Controls, Workload Identity, Cloud KMS/CMEK, Secret Manager, and DLP.
Experience advising on pipeline observability and data-quality approaches on GCP: Cloud Monitoring/Logging, traceability for Dataflow/Dataproc jobs, and data validation frameworks.
Experience participating in at least one GCP-focused data platform migration or modernization as an advisor, reviewer, or core contributor.
Strong verbal and written communication skills; ability to produce concise, actionable guidance and explain technical tradeoffs to engineering and non-engineering stakeholders.
Familiarity with Terraform and CI/CD patterns for GCP resources and data pipelines; conceptual understanding of GitOps for data workloads.
Certifications such as Google Professional Data Engineer or Google Cloud Architect are a plus but not required.
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 Seven (5 - 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 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.
Pay: US: $8,007.29/mo - $18,149.85/mo, CO: $8,007.29/mo - $17,393.61/mo, MD: $8,452.14/mo - $18,149.85/mo, NJ: $8,452.14/mo - $18,149.85/mo, NY: $8,452.14/mo - $18,149.85/mo, NYC: $10,231.53/mo - $18,149.85/mo, WA: $8,452.14/mo - $18,149.85/mo
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)
Employee Polygraph Protection Act
E-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E-Verify program:
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Right to Work Notice (English) / (Spanish)
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