Enterprise Account Executive, Tiger Data

• 4-6 years of experience in a full-cycle quota-carrying Saas sales role
• Technical background with a deep understanding of database technology, data platforms, open source, and cloud ecosystems
• Ability to articulate the business value of complex enterprise technology
• Proficiency in modern sales software (e.g., Salesforce, Outreach, etc.)
• Proven track record of exceeding sales targets and driving revenue growth
• Strong interpersonal skills with the ability to build rapport and establish trust with customers
• Demonstrated ability to manage multiple priorities in a fast-paced environment effectively
• Entrepreneurial mindset with a proactive approach to problem-solving

Desirable

• As an Enterprise Account Executive, you will interact directly with customers, answer questions, provide information, and build connections with them
• In this role, you will be at the forefront of closing deals and providing solutions in our growing database market
• You can make a meaningful impact by helping organizations harness the power of data for better decision-making, improved operations, and innovation
• You will manage complex sales relationships with critical customers and effectively communicate the value of Timescale
• As the company and industry expand, we see our high-growth market as an opportunity for significant sales and career advancement
• Focus on closing Sales Qualified Leads (SQL) from the marketing team
• Qualify leads based on their challenges and alignment with our product suite
• Strategically prospect into CTOs, Engineering Leads, and Technical End Users
• Manage the full sales cycle
• Leverage resources such as Sales Engineers and Data Architects effectively
• Guide relevant stakeholders through the sales process to close
• We share our tech stack with the caveat that we don’t require previous experience in it (but a love of these languages is helpful): Timescale's tech stack includes TimescaleDB, built on PostgreSQL, along with AWS, Go, Docker, Kubernetes, and Python for scalable time-series data management