Qualtrics Job - 40888463 | CareerArc
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Company: Qualtrics
Location: Seattle, WA
Career Level: Mid-Senior Level
Industries: Technology, Software, IT, Electronics

Description

The Qualtrics XM Platform™ is a system of action that helps businesses to attract customers who stay longer and buy more, to engage and empower employees to do the best work of their lives, to develop breakthrough products people love, and to build a brand people can't imagine living without.


Joining Qualtrics means becoming part of a team bold enough to chase breakthrough experiences - like building a technology that will be a force for good. A team committed to diversity, equity, and inclusion because of a conviction that every voice holds value, with a vision for representation that matches the world around us and inclusion that far exceeds it. You could belong to a team whose values center on transparency, being all in, having customer obsession, acting as one team, and operating with scrappiness. All so you can do the best work of your career.


We believe every interaction is an opportunity. Are we yours?

The Challenge

The mission of the iQ team is to bring intelligence into the Qualtrics platform and products. We build a large suite of analytics tools built directly into the Experience Management (XM) PlatformTM that automatically analyze experience data 24/7 to proactively spot opportunities for improvement, recommend the actions to take and automate the relevant tasks and the actions. 

We are looking for talented and innovative data engineers to bring our Machine Learning platform to the next level. Our goal is to personalize the Qualtrics experience using ML features showcasing Qualtrics data as a core value proposition and competitive advantage.

The Role

As a Sr. Data Engineer, you should love building highly available, scalable, secure and efficient (big) data systems. You will:

  • Work as part of a multidisciplinary team to research, implement, evaluate, optimize, productize and maintain data systems that empower cutting-edge machine learning models to meet the demands of our rapidly growing business
  • Stay on top of the latest developments in data engineering and big data technologies
  • Partner closely with, and incorporate feedback from other engineering and infrastructure teams, product specialists, product managers, executives and other stakeholders
  • Lead and engage in design reviews, modelling discussions, requirement definitions and other technical activities in diverse capacity
  • Lead greenfield R&D projects and contribute to the long-term vision of the R&D organization

Basic Qualifications 

  • BS, or MS in Computer Science or related fields
  • 5+ years of experience as a Data Engineer or SDE
  • 3+ years of experience designing, optimizing and troubleshooting ETL solutions.
  • Demonstrated knowledge and experience in various storage, management and database technologies
  • Strong understanding of distributed processing frameworks and programming models (Hadoop, Hive, Hbase, Spark, EMR, Dask, etc.) that help  processing of large-scale, complex datasets.
  • Experience with relational SQL and large scale data processing with NoSQL databases.
  • Experience with workflow management platform like Apache Airflow, Kubeflow, Argo etc
  • Hands-on experience and advanced knowledge of SQL
  • Experience with at least one modern programming language (e.g., Scala, Python, Java) and scripting.
  • Proficiency in all aspects of the software development cycle.
  • Curious, self-motivated & a self-starter with a ‘can do attitude'. 
  • Excellent communication, writing and presentation skills

Preferred Qualifications 

  • Excellent interpersonal and communication skills
  • 3+ years of hands-on experience working with distributed data technologies (e.g. Hadoop, MapReduce, Spark, Flink, Kafka, etc.) for building efficient & large-scale data pipelines.
  • Experience or willingness to learn working on the AWS big-data stack.
  • Familiarity with theory and practice of machine learning, in particular the machine learning life cycle 
  • Comfortable working in a fast paced, highly collaborative, dynamic work environment.
  • Experience in mentoring engineers and scientists on complex technical issues
  • Experience in machine learning systems (e.g. SageMaker, MLFlow), and deep learning frameworks  (e.g. TensorFlow, PyTorch, MXNet etc) 
  • Experience with container orchestrators like Kubernetes, Nomad etc.


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