Data Engineer Lead

Job Description: 

• understanding in depth both the business and technical problems Dataworks aims to solve - building tools, platforms and pipelines to enable teams to clearly and cleanly analyze data, build models and drive decisions; 

• scaling up from “laptop-scale” to “cluster scale” problems, in terms of both infrastructure and problem structure and technique; 

• delivering tangible value very rapidly, collaborating with diverse teams of varying backgrounds and disciplines; 

• codifying best practices for future reuse in the form of accessible, reusable patterns, templates, and code bases; 

• interacting with senior technologists from the broader enterprise and outside of FedEx (partner ecosystems and customers) to create synergies and ensure smooth deployments to downstream operational systems.
   

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.  

Minimum Experience: 

• 5 - 7' years equivalent work experience in information technology or engineering environment ;

• 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, both Polish and English; 

• a related advanced degree may offset the related experience requirements.  
   

Knowledge, Skills and Abilities:  

• technical background in computer science, software engineering, database systems, distributed systems;  

• fluency with distributed and cloud environments and a deep understanding of how to balance computational considerations with theoretical properties; 

• detailed knowledge of the Microsoft Azure tooling for large-scale data engineering efforts and deployments is highly preferred;  

• a compelling track record of designing and deploying large scale technical solutions, which deliver tangible, ongoing value;  

• direct experience having built and deployed robust, complex production systems that implement modern, data scientific methods at scale;  

• ability to context-switch, to provide support to dispersed teams which may need an “expert hacker” to unblock an especially challenging technical obstacle, and to work through problems as they are still being defined; 

• demonstrated ability to deliver technical projects with a team, often working under tight time constraints to deliver value;  

• an ‘engineering’ mindset, willing to make rapid, pragmatic decisions to improve performance, accelerate progress or magnify impact;  

• comfort with working with distributed teams on code-based deliverables, using version control systems and code reviews;  

• ability to conduct data analysis, investigation, and lineage studies to document and enhance data quality and access;  

• use of agile and develops practices for project and software management including continuous integration and continuous delivery; 

• strong hands-on experience with Spark (PySpark, Scala) and Databricks for large-scale distributed data processing and analytics;

• proven track record using Kafka and Structured Streaming, particularly with Azure Event Hubs for real-time data ingestion; 

• solid background in working with SQL and NoSQL systems, Python data stack, Java, Kubernetes; 

• practical experience with Terraform for infrastructure as code and cloud resource provisioning.