Marketing Associate, Databento

• The ideal candidate can communicate with ease on various platforms (social media, blog posts, technical content, press releases, conferences, etc). Below are other skills that we look for
• 0-2 years of internship and/or working experience in B2B SaaS in areas like SEO, digital marketing, growth marketing, social media, and/or PR. We will likely reject those with too many years of experience, as this is meant to be an entry-level position (see salary details below)
• A natural interest in posting and engaging frequently with our community of quants and developers on LinkedIn and Twitter
• Knowledge of CRMs like Hubspot, as well as SEO metrics, growth metrics, and content analytics
• An interest in quant trading, market data, and/or the financial industry
• A desire to work at a fast-growing fintech startup

• You'll help oversee the company's GTM, digital media, growth metrics, and overall marketing plan
• Lead or co-lead social media on Linkedin and Twitter, including valuable short-form posts, long-form technical blog posts, videos, polls, and more
• Drive ideas and efforts to create PR, partnerships, case studies, and co-marketing opportunities with other companies in our industry
• Improve the company's SEO and SEM strategy, as well as other digital ad placements, backlink opportunities, and website optimizations
• Execute various inbound and outbound campaigns; track relevant metrics to determine the ROI of campaigns and marketing channels
• Research competitors and their marketing strategies
• Collaborate with executives to make decisions regarding the budget, branding, and direction of the company, including high-level decisions regarding agencies
• Create and maintain a successful brand and image

• 401k
• Health and dental insurance
• Unlimited PTO

Total funding: $66.1m

Despite the exponential growth of data within fintech, the majority of financial data providers continue to provide inconvenient services, with high licensing fees, slow infrastructure, and difficult onboarding processes. Furthermore, taking a new dataset to production requires high cost commitment and a large lead time, which can be difficult to manage.