Seamless.AI has been delivering the world's best sales leads since 2015. Our product is the first real time, B2B search engine helping sales teams maximize revenue, increase sales, and easily acquire their total addressable market using artificial intelligence. We have been recognized as one of Ohio’s fastest growing companies and won 2020 Best Places to Work, LinkedIn’s Top 50 Tech Startups in 2020 and 2022, and Purpose Jobs 2023 Best Workplace Culture and Best Work-life balance.
We are seeking a highly experienced and talented Vice President of Data Engineering to join our fast-growing SaaS company. The ideal candidate will have a proven track record in building robust data pipelines and processes, along with a strong background in AWS, Python, and data consolidation techniques. Reporting directly to the CTO, this strategic role requires excellent communication and organization skills, as well as the ability to lead and mentor a team.
Responsibilities:
- Lead the design, development, implementation, and maintenance of scalable data pipelines and processes to support our rapidly growing SaaS platform.
- Oversee the consolidation, validation, and transformation of data from various sources into a unified and reliable format.
- Collaborate closely with cross-functional teams to understand business requirements and provide data solutions that meet their needs.
- Manage and mentor a team of data engineers, fostering a collaborative and high-performance work environment.
- Develop and enforce best practices for data engineering, including coding standards, documentation, and quality assurance processes.
- Stay up-to-date with industry trends, emerging technologies, and advancements in data engineering techniques, and assess their potential impact on our business.
- Effectively communicate complex technical concepts to non-technical stakeholders, including executives and other department heads.
- Take ownership of the data engineering budget, including resource allocation, vendor management, and cost optimization.
- Analyze and report on the ROI of data engineering initiatives, identifying areas for improvement and recommending actionable strategies.
- Track team and individual performance metrics, providing constructive feedback and implementing performance improvement plans as necessary.
