Sales Engineer

QuickLaunch Analytics

1d•$120,000 - $140,000•Remote

About The Position

We are seeking a Sales Engineer to support our go-to-market (GTM) efforts. In this role, you'll collaborate with enterprise prospects and customers as well as our partners to showcase the value of our solutions. You'll partner with the Sales team to identify customer needs, define solutions, and demonstrate how QuickLaunch can drive measurable business outcomes. Then you will work with both functional and technical customer stakeholders to ensure our solution is scoped and packaged correctly to drive rapid implementation, customer adoption, and success. If you enjoy helping organizations make sense of their data using modern tools like Power BI, and you're comfortable navigating ERP systems, this may be the perfect fit.

Requirements

Technical Knowledge of Power BI, Lakehouse architecture, modern data stack technologies (e.g. Databricks, Microsoft Fabric, Fivetran), and ERP systems.
Strong data understanding of ETL processes, data transformation, movement, harmonization, and considerations around latency and performance. With demonstrated knowledge of designing and maintaining ETL/ELT pipelines leveraging Databricks, Apache Spark, and Microsoft Fabric Dataflows and Pipelines.
Demonstrated deep technical knowledge of Microsoft Power BI, encompassing semantic modeling, advanced DAX, Power Query (M), data visualization best practices, and enterprise deployments.
Familiarity with Medallion Architecture, designing and maintaining Bronze, Silver, and Gold data layers to support reliable, scalable analytics pipelines from raw ingestion to business-ready datasets.
Excellent communication, storytelling, and presentation skills, with a strong ability to build and maintain client relationships.
Team-Oriented attitude with a willingness to adapt, collaborate, and thrive in a dynamic environment.
Analytics expertise, including reporting tools, Excel modeling, BI use cases, and cube-based or semantic layer design.
Go-To-Market understanding — familiarity with sales cycles, buyer personas, pricing models, and how to position technical solutions as business value.
Continuous Learning: Stay current on new features and best practices in Databricks, Microsoft Fabric, Delta Lake, and Al/Data Science; contribute to internal knowledge-sharing.
Bachelor’s degree in Computer Science, Information Systems, Business, or a related field—or equivalent professional experience.

Nice To Haves

Experience supporting enterprise software or consulting sales in a pre-sales or solution engineering capacity.
Demonstrated understanding of data lakehouse architectures, Delta Lake, and Microsoft Fabric, along with proficiency in SQL, Python, or Spark to support scalable data engineering and analytics solutions.
Background working with clients in data-intensive industries such as manufacturing, consumer goods, distribution, or professional services.
Hands-on experience with data modeling, Power Query, or DAX is a plus.
Previous experience with MEDDPICC
Experience working at a smaller stage company / start-up environment

Responsibilities

Partner with sales team members throughout the sales cycle to provide technical expertise, solution design, and value-based demonstrations.
Conduct discovery sessions with prospects and customers to understand business goals, data challenges, and analytics use cases.
Translate technical capabilities into clear, compelling solutions that align with customer needs and pain points.
Deliver product demonstrations, technical workshops, and proof-of-concepts to showcase the value and capabilities of QuickLaunch.
Partner cross-functionally with Product, Engineering, and Customer Success teams to address technical challenges, ensure seamless handoffs, and drive customer success.
Stay up to date with industry trends, competitive landscape, and emerging technologies trends.
Provide feedback to the product team on customer needs, pain points, and potential product enhancements
Collaborate with team to provide technical responses to RFPs, security questionnaires, and integration-related questions.
Build a deep understanding of our product offerings, time to value, value proposition, customer use cases, and analytical use cases.
Educate internal stakeholders on product updates and technical best practices.