Data Engineer III

Key Skills

Apache Cassandra, Apache Kafka, HDFS, Jupyter Notebook, matplotlib, MongoDB, MySQL, NoSQL, Pandas, Postgres, PySpark, Python Programming, Scala Programming, Scikit-learn, Spark-SQL, SQL, TensorFlow

Job Description

The Data Engineer III plays a pivotal role within Dataworks, focused on driving engineering innovation within Dataworks, helping define and build the Dataworks organization and leading 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 III will contribute to the adherence of technical standards for data engineering, including the selection and refinements of foundational technical components. S/he will work on those aspects of the Dataworks 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. S/he will also provide mentorship and guidance to junior engineers to support the continued training and up-skilling of the Data Engineering team.

Essential Job Duties & Responsibilities:

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

Championing the adherence to 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

Skill/ Knowledge Considered a Plus:

Technical background in computer science, software engineering, database systems, distributed systems

Fluency with distributed and cloud environments and a strong 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 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 devops practices for project and software management including continuous integration and continuous delivery

Demonstrated expertise working with some of the following common languages and tools:

Spark (Scala and PySpark), HDFS, Kafka and other high volume data tools

SQL and NoSQL storage tools, such as MySQL, Postgres, Cassandra, MongoDB and ElasticSearch

Pandas, Scikit-Learn, Matplotlib, TensorFlow, Jupyter and other Python data tools

Minimum Qualifications:

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 (5) 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 senior member of multi-functional project teams.

Strong oral and written communication skills.

A related advanced degree may offset the related experience requirements.