Analytics Engineer

Retool has set out to radically rethink how this custom internal software is built. We’ve created a new type of development environment, combining visual, drag-and-drop manipulation with code-based customization. It seamlessly integrates with nearly any data source and enables instant deployment to end-users. It’s a force multiplier for developers building internal tools, dramatically faster and easier than writing software from scratch.

Retool is a fast-growing company with quickly evolving business needs. We’re looking to hire an Analytics Engineer to help us build out our data warehouse and business intelligence systems to serve the needs of our business today and for a broader scale years from now. We're looking for someone who is ready to get their hands dirty, is motivated by having an impact on the business, and is constantly curious. This is the right role for someone who thrives while making sense of the blurry space that is data at a high growth startup.

What You'll Do:

As an Analytics Engineer, you will build a foundation that strengthens Retool’s data culture at scale. You’ll initiate projects that solidify Retool’s data capabilities and help the company remain data-driven. Our data team is in its early stages, so you will have the opportunity to help define our data team structure and operating rhythm for the future. You’ll develop data sets to streamline operations, inform strategic priorities, and develop product insights. We’ll look to you to define key metrics and develop dashboards that empower stakeholders to make data-informed decisions. You’ll take on ownership of our data stack to ensure that your teammates are able to access the data they need to make decisions and technical teams are able to quickly implement events. We’ve already built out a solid stack on top of Segment, Databricks, dbt, and of course, Retool, but we need your help to ensure it scales with the company as our user base grows.

Who You'll Work With:

As part of our data team you’ll work with stakeholders across the business, including finance, marketing, engineering, product, operations, and support. You’ll be joining a broader team of Retools who are passionate about serving our customers, enjoy collaborating to build an incredibly innovative product, and enjoy swapping stories. If this sounds like you, we’d love to hear from you!

In This Role, You'll:

Use dbt to architect, build, and maintain a scalable data warehouse on top of Databricks Delta Lake
Create documentation, data quality systems, and best practices to ensure Retool’s data warehouse is accessible and reliable
Design our Analytics and Business Intelligence architecture, assessing and implementing new technologies where fitting
Build and maintain scalable data pipelines
Work with our engineering teams to validate existing data sources, identify opportunities to improve data quality, and craft requirements for robust new instrumentation as needed
Develop dashboards and define metrics that drive key business decisions
Partner with business teams to deploy robust reports and analyses, surfacing key insights that shape Retool’s future direction

The Skillset You'll Bring:

Background in Analytics Engineering, Business Intelligence, Data Engineering, or Data Analytics
Ideally 3+ years of experience managing and building a large complex data model across a range of sources
Experience implementing and defining data best practices at scale
Strong SQL skills including advanced syntax, query & table storage optimization techniques, and manipulation of complex data types like JSON
Excellent business acumen with the ability to translate stakeholder requirements into data models
Hand-on knowledge of ETL and data warehouse technologies (i.e. dbt, BigQuery, Redshift, Airflow, Snowflake)
Skilled at data visualization, with strong opinions on the right way to distill information to various audiences
Comfortable with common git workflows and at least one scripting or statistical programming language (ideally Python and/or Scala)
A solution-oriented growth mindset. You’ll need to be a self-starter and thrive in a dynamic environment
A bias towards communication and collaboration with business and technical stakeholders
Quantitative rigor and systems thinking

‌