Our Data Engineering & Operations team is a force multiplier for data practitioners at Pluralsight. We provide platform, tooling and datasets to make Pluralsight a data-driven organization.
Our work includes: building a data platform to support our product data scientists, data engineers and data analysts; replicating product data from our streaming platform to our data lake and warehouse; deploying data science models; and maintaining existing data infrastructure. You’ll have the opportunity to work with data tools, like SQL, Python, Spark and Kafka, as well as platform tools like Kubernetes, Docker, AWS, and Terraform.
Who you’re committed to being:
What you’ll own:
You utilize a multidisciplinary approach to providing solutions for the business, combining technical, analytical, and domain knowledge.
You have strong development skills, experience transforming and profiling data.
You understand the benefits and risks of a variety of data technology solutions, which guide your implementation decisions.
You love interfacing with data scientists and analysts to understand their needs.
You have an eagerness to dive in to data sources to understand availability, utility, and integrity of our data
You are passionate about cloud computing and are tailing emerging technologies for data processing and machine learning at scale in the cloud
Experience you’ll need:
Developing platform, tooling and solutions for data practitioners using a deep understanding of their objectives and pain points
Building and maintaining production data pipelines for data science and analytics
Improving observability in our data environment, including uptime, usage, data quality, and data freshness
Building production applications from data science research and exploratory analytical work
5+ years of taking a multidisciplinary approach to data development: we emphasize picking the right tool for the job
Deep experience with a number of data tools: e.g. SQL, Spark, Hadoop, Python
Experience with cloud infrastructure and infrastructure as code
Experience with stream processing (spark, flink, kafka streams, etc) and orchestration frameworks (airflow, argo, etc) is desirable
Managed systems with complex dependency management and orchestration requirements
Strong capability to manipulate data with big data characteristics: volume, velocity and variety
Effective communication skills with technical team members as well as business partners. Able to distill complex ideas into straightforward language
Ability to problem solve independently and prioritize work based on the anticipated business value