Senior Data Engineer, Pluralsight

• 5+ years of experience designing and delivering data warehouses and marts to support business analytics
• Hands on experience with dbt (certification preferred)
• Solid foundation in SQL development on RDBMS (Snowflake and Postgres preferred)
• Experience with dimensional data modeling/data workflow diagrams, using Kimball methodology (conceptual, logical, and physical)
• Experience with source control and deployment workflows for ETL (dbt/ airflow preferred)
• Hands on experience with scripting languages (Python, BASH, etc)
• Experience with metadata management and data quality
• Knowledge of software engineering standard processes with experience with implementing CI/CD (Gitlab, Github Actions, Teamcity, etc.), monitoring & alerting for production systems
• Strong capability to manipulate and analyze complex, high-volume data from a variety of sources
• Good experience crafting and building end-to-end data models and pipelines as well as alerting
• Knowledge of data management fundamentals and data storage principles
• Experience in data modeling for batch processing and streaming data feeds; structured and unstructured data

Desirable

• Our Data Engineering team, within our Data Services Organization, builds and maintains the infrastructure essential to delivering high-volume, business-critical data to the organization to enable data-driven decisions
• We are focused on expanding our curated and modeled data that unify sources of truth across our multiple products and domains
• You’ll have the opportunity and empowerment to guide the team on best practices using modern distributed data tools like Snowflake, Spark, Kafka, and dbt
• This is an ideal opportunity for someone that has strong opinions on how things should be done and loves figuring out what the right solution is for the scenario at hand
• Your voice will be heard and will be given the opportunity to make an impact with the direction and delivery of our data platform to internal stakeholders
• Data Warehousing and modeling delivery
• Support and evolution of data environment to deliver high-quality data, speed, and availability
• Curation of source-system data to deliver trusted data sets
• Involvement on data cataloging and data management efforts
• Production ETL performance tuning and environment-level resource consumption and management
• Migration of POC pipelines to production data processes