About AlphaSense: 

AlphaSense is a search engine for market intelligence that transforms how decisions are made by the world’s leading corporations and financial institutions. Leveraging AI and NLP technology, AlphaSense enables knowledge professionals to extract insights in seconds from thousands of disparate data sources, including company filings, event transcripts, news and trade journals, and equity research. Founded in 2011 and backed by investors including Innovation Endeavors and Soros Fund Management, AlphaSense is on a mission to empower organizations to make data-driven decisions with confidence.

About the Team:

The BI & Analytics team works across AlphaSense to make data an asset for growth. We do that through infrastructure, process, well-modeled and documented data, analysis, and deep partnership with the business. We own and manage the data stack, consisting of Segment, Fivetran, Stitch, Dataform, BigQuery and Looker. In addition to technical leadership, we partner across departments to raise the analytics bar, drive operational improvement, and support growth. Our work spans BI dashboards for business and executive users, ML models for critical business needs, and data & infrastructure to support our AI engineers. 

About the Role: 

We are looking for a curious, collaborative, and high-output data pro to join our team as a Lead Data Engineer. This position reports to our VP, Business Intelligence / Data & Analytics.

Who You Are:

• 5+ years of experience successfully delivering data engineering solutions which enable business insights and data-driven capabilities
• Strong problem solver and collaborator with a record of getting big things done
• Self-starter with the ability to think and operate enterprise-wide in order to implement holistic systems and solutions supporting critical business needs
• History of leading and influencing across technical and non-technical audiences
• BS or MS degree in Computer Science, Engineering, or a related technical field

What You’ll Do: 

• Lead the design, build, and scaling of our backend data infrastructure (across data acquisition, pipelines, warehousing, etc.) using the latest in data engineering technologies such that the data platform is reliable, extendable, and performant
• Establish systems to monitor data availability, quality, and stability
• Translate core business intelligence, analytics, and data science business requirements into usable, scalable data models and systems
• Partner broadly across the business in order to ensure a cohesive platform which enables the whole enterprise’s data and analytics needs across Product, Marketing, Sales, Ops, Customer Success, Finance, etc.
• Shape the future of data engineering, leveraging best practices and improving what we do and how we do it
• Coach, mentor, and develop data engineers and other peers in an agile team environment to enable and grow a high-performing team