Analytics Engineer

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Requirements:

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

Responsibilities:

• 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.
• 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 data documentation and data discovery initiatives.