Analytics Engineer

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Responsibilities

• DWH & transform architecture: expand and own our transformation layer in dbt & BigQuery. Enable analyst users with accurate and accessible data via a logical, intuitive, and documented data mart. 
• Improve data quality by bringing a deep understanding of business needs into the transformation process and rigorous software engineering best practices to analytics code.
• Document & build processes to share knowledge from table and column definitions to key transforms logic, workflow schedules, etc.
• Partner with technical stakeholders (Data Science, Product, Engineering) to align on analytics roadmap. Plan for product or schema changes that could impact our analytics model and determine solutions for keeping our data accurate and updated. Surface requests for new data/upstream changes to improve our models. 
• Enable business and data science users via datasets that support machine learning applications like product usage forecasts, customer health scoring, etc.
• Maintain data ops best practices including ownership of our git repository and implementation of a code review practice.
• Train and onboard business and analyst users to Looker reporting and to underlying datasets/models, respectively. 
• Build occasional reporting in Looker to address key business questions.

Qualifications

• Knowledgeable and passionate about the “modern data stack” and related processes/capabilities. Candidate should be an advocate for unlocking value and driving efficiency with these tools/frameworks. 
• 1+ year experience owning dbt in a high-growth org required, including deploying capabilities such as utils, packages, tests, snapshots, and incremental tables. 
• Excellent SQL skills. 3 years SQL experience required, with regular use of advanced functions in a production environment.
• 1+ years experience designing and scaling a cloud data warehouse (BigQuery preferred) for business intelligence & analytics applications. 
• Excellent communication skills and habits. Ability to translate business problems and requirements into data projects (both architecture and analysis/visualization) and communicate results back to non-technical audiences. 
• Strong adherence to data ops best practices, including version control (e.g., GitHub), and data testing (dbt). 
• Excellent prioritization skills. Strong point of view on where the data org creates leverage, what data assets can drive results, and how to enable users to use those assets.
• 1+ years experience with Looker strongly preferred (LookML, front-end and administration ideal), working knowledge of Airflow, GCP tooling (Dataflow, k8s) and Python a plus.