Data Engineer

About the role

You will own the end-to-end data pipelines that feed the Skimmer product, designing and maintaining the data models and transformations that ensure analytics remain accurate and timely for customers. In this capacity, you will be responsible for building the embedded reporting experiences that allow pool professionals to understand, analyze, and grow their businesses directly inside the application. The role requires a partnership mindset, as you will work closely with product, engineering, and business stakeholders to translate ambiguous requirements into structured, executable data solutions. You are expected to bring sound judgment and a high degree of autonomy, making decisions on architecture and quality with minimal oversight. This position sits at the intersection of data platform engineering and customer-facing analytics, giving you exposure to both infrastructure and user experience. You will play a key role in ensuring that data is not only available but also trustworthy, performant, and well-documented for a growing team.

Key facts

What you'll do

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations.
• Build and maintain customer-facing data models and reports in Sigma Computing, embedding analytics directly within the Skimmer application to drive user decisions.
• Manage and extend ingestion using Fivetran across a growing set of sources, ensuring new data connections are reliable, scalable, and secure.
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse, optimizing queries for performance and cost.
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences that integrate seamlessly into the product.
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture from point of origin.
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise to minimize disruption for end users.
• Document data models, definitions, and architecture to support a growing team and enable long-term maintainability.
• Contribute to the evolution of the data platform by evaluating new tools, patterns, and best practices that improve scalability and developer experience.
• Participate in code reviews and technical discussions to uphold engineering standards and share knowledge across the data team.
• Work on prioritization and execution in an agile environment, balancing short-term requests with long-term platform improvements.
• Act as a technical liaison between analytics and operations, ensuring that data workflows align with business processes and SLAs.
• Identify opportunities to automate manual reporting tasks, reducing overhead and increasing reliability for downstream users.
• Support the rollout of new analytics features, ensuring proper testing, rollout strategies, and post-launch validation.

Requirements

• Disciplined Problem-Solving: Proven ability to decompose complex business problems into structured, executable plans. You prioritize understanding the 'why' and 'how' of data before implementation.
• Proactive Communication: Comfortable identifying ambiguity and proactively seeking clarity from stakeholders rather than making assumptions.
• Ownership of Quality: A strong sense of ownership - you don't consider a task 'done' until it is validated, performant, and meets the documented requirements.
• Demonstrated experience in data engineering, with a track record of owning data infrastructure end to end.
• Strong SQL skills and hands-on experience with a cloud data warehouse (Snowflake preferred).
• Proficiency in Python for data transformation and automation.
• Experience working with C#/.NET application environments and partnering with application engineers on source systems.
• Experience with managed ingestion and transformation tooling (Fivetran, dbt, or similar).
• Hands-on experience building data models and reports in a BI/analytics platform (Sigma Computing a strong plus), ideally embedded in a customer-facing product.
• Proficiency with AI coding tools like Claude or Cursor to work efficiently and raise your output.
• Solid understanding of data modeling concepts and ELT/ETL best practices.
• Strong communication skills and comfort working with both technical and non-technical partners.
• Ability to work independently and make decisions that impact data quality and user experience.
• Comfort navigating complex data environments with multiple sources, schemas, and dependencies.
• Willingness to follow established processes while also contributing ideas for improvement.

Nice to have

• Experience with embedded analytics or customer-facing reporting at scale.
• Experience building data products in a SaaS or product-led company.
• Experience with rigorous code review processes and contributing to maintaining high engineering standards within a team.

Practical notes

Remote work friendly.

Candidates must be eligible to work in the United States.

Skimmer has downtown office spaces in Austin, TX and Toronto, ON, and remote-first employees around the US. Right now we can hire hybrid employees in Austin and Toronto, or remote employees in the following states: Alabama, California, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, and Wisconsin.