Analytics Engineer

Model and analyze data utilizing SQL best practices for OLAP / OLTP query and database performance
Leverage Data Build Tool (DBT), Snowflake, Airflow, AWS infrastructure, CI/CD, testing, and engineering best practices to accomplish your work
Generate innovative approaches to datasets with millions of daily active users and terabytes of data
Translate business requirements for near-real-time actionable insights into data models and artifacts
Communicate findings clearly both verbally and in writing to a broad range of stakeholders
Administrative duties for Snowflake, Tableau, and DBT/Airflow infrastructure
Test, monitor, and report on data health and data quality
Lead the charge on data documentation and data discovery initiatives

Are proficient in SQL and understand the difference between SQL that works and SQL that performs
Have worked with data modeling and orchestration tools
Have experience with relational (SQL), non-relational (NoSQL), and/or object data stores (e.g., Snowflake, MongoDB, S3, HDFS, Postgres, Redis, DynamoDB)
Have a solid understanding of ETL vs. ELT processes, data warehouses, and business intelligence tools
Have prior experience clearly communicating about data with internal and external customers
Are highly motivated to work autonomously, with the ability to manage multiple work streams
Interest in building and experimenting with different tools and tech, and sharing your learnings with the broader organization
Have developed and maintained DBT or Airflow in production environments
Have experience programmatically deploying cloud resources on AWS, Azure, or GCP
Have successfully implemented data quality, data governance, or disaster recovery initiatives
Are proficient in at least one imperative programming language (i.e., Python)

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