U

University of Arkansas

Public research university in Arkansas

Data Engineer 4

Data EngineeringConfirmed live in the last 24 hours
Full-timeLittle Rock, AR, USA$95k

About the Job

Requirements

  • A master's degree in Computer Science, Information Science, or a related discipline is required; a PhD degree in Computer Science, Information Science, or a related discipline is an alternative.
  • At least 4 years of experience as a data engineer, software developer, software engineer, database administrator, or in a similar role is required, unless the candidate qualifies through the PhD alternative.
  • At least 2 years of experience leading a team of data engineers is required.
  • Proficiency in data models, data pipelines, ETL processes, data stores, data mining, and segmentation techniques is required.
  • Proficiency in programming and scripting languages, including Java and Python, is required.
  • Proficiency in data integration platforms and SQL database design is required.
  • Proficiency in numerical analysis, analytical methods, and data security is required.
  • Proficiency in collecting raw data from social media platforms is required.
  • Proficiency in creating continuous integration and continuous deployment pipelines is required.
  • Proficiency in front-end development, including HTML, CSS, JavaScript, and Node.js, is required.
  • Proficiency in training and deploying machine learning models on datasets is required.
  • Ability to lead a large team of at least five data engineers is required.
  • Proficiency with Kafka, MongoDB, and Kubernetes clusters is required.
  • Proficiency with microservices, Python, Golang, FlaskAPI, GraphQL, and Docker is required.
  • Proficiency with Elasticsearch, Grafana, Prometheus, and Kibana is required.
  • Proficiency in data modeling concepts, including entity-relationship diagrams, dimensional modeling, and Data Vault, and data APIs such as RESTful APIs is required.
  • Proficiency in data processing software such as Hadoop, Spark, TensorFlow, Pig, and Hive, and algorithms such as MapReduce and Flume is required.
  • Proficiency in cloud platforms such as AWS, Azure, and GCP, and data warehousing solutions such as Snowflake, Amazon Redshift, Google BigQuery, and Azure Synapse is required.
  • Technical communication skills are required.

Responsibilities

  • Lead a team of data engineers.
  • Collect and analyze raw data from various sources, including social media platforms.
  • Organize and maintain datasets.
  • Improve data quality and process efficiency.
  • Design and manage ETL pipelines that process more than 10 million data points daily, using Kafka for real-time data streaming and MongoDB for NoSQL storage across Kubernetes clusters.
  • Design and deploy scalable microservices in Python and Golang using FlaskAPI, GraphQL, and Docker, supporting sub-second response times and concurrency with goroutines.
  • Migrate large volumes of data from legacy databases to MongoDB to achieve sub-second access latencies and optimize storage for unstructured data through Elasticsearch integration.
  • Set up and manage infrastructure for data ingestion, processing, and storage.
  • Evaluate model needs and objectives, and interpret data trends and patterns.
  • Conduct complex data analysis and report results.
  • Prepare data for analysis and reporting by transforming and cleansing it.
  • Combine raw information from different sources.
  • Identify ways to improve data quality and reliability.
  • Identify opportunities for data acquisition.
  • Develop analytical tools and programs.
  • Collaborate with COSMOS teams on multiple projects.
  • Manage services and operational infrastructure to support system reliability and resiliency.
  • Create continuous integration and continuous deployment pipelines using Jenkins and GitLab CI to automate service and system deployment.
  • Integrate Prometheus for monitoring, Grafana for real-time dashboarding and visualization, and Kibana for log analysis sourced from Elasticsearch.
  • Perform front-end development using technologies such as HTML, CSS, JavaScript, and Node.js.
  • Train machine learning models on datasets.
  • Deploy machine learning models.
  • Improve system fault tolerance by incorporating alerting mechanisms.
  • Develop frameworks such as Spring Boot and React.
  • Perform other tasks as assigned.

About the Company

University of Arkansas

Public research university in Arkansas

University of Arkansas is a public land-grant research university in Fayetteville, Arkansas. It provides undergraduate and graduate education, conducts research, and supports public-service and extension activity. Academic colleges, laboratories, student services, facilities, and administrative units make up the campus operating environment. Faculty, research, student-support, technical, trades, and professional roles support the institution. The profile is campus-specific while recognizing that the University of Arkansas System uses a shared official careers platform.

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Headquarters
Salt Lake City, UT
Founded
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