WHY WE'RE LOOKING FOR YOU:

Retool is a fast-growing company with quickly evolving business needs. We’re looking to hire engineers to help us build out our data ecosystem to serve the needs of our business today and for 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:

You’ll design and build a foundation that strengthens Retool’s data culture at scale. You’ll take on projects that solidify Retool’s reporting capabilities and help the company remain data-driven. You'll develop and scale ingestion infrastructure, optimize our ETLs, and design our data architecture for scale, with a keen eye for data warehouse management and tooling. You'll also 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:

You’ll work with stakeholders across the business, including data scientists, 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:

• Architect and scale a modern data platform that will be used internally by all of Retool
• Build and maintain scalable ETL pipelines to efficiently process and transform large volumes of data from source systems into our data warehouse
• Work with our engineering teams to ensure robust instrumentation across areas of the product
• Partner with business stakeholders to synthesize and develop requirements for core tables
• Implement monitoring and observability to guarantee data quality and consistency
• Articulate and implement best practices around ingestion frameworks and data pipeline development

THE SKILLSET YOU'll BRING: 

• Background in Data Engineering, 5 years of experience building and maintaining scalable data infrastructure, including distributed processing solutions (e.g. Spark), cloud-based data lakes and warehouses (e.g. Databricks, Snowflake, BigQuery), workflow management (e.g. Airflow, Luigi), and data transformation tools (e.g. DBT) 
• Experience using Infrastructure as Code (IaC) to automate data infrastructure provisioning and management (i.e. Terraform)
• Experience implementing and defining best data practices at scale
• Experience proactively identifying opportunities to improve ETL & dashboard performance and cost
• Excellent business acumen with the ability to translate stakeholder requirements into data models
• Proficiency with common git workflows and at least one programming language (e.g. Python, Scala, Java)
• 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