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Mark Schnitzer
Build market leading resilient and efficient cloud big data financial solutions for enterprises. Fintech Business and Engineering Visionary and Leader. Entrepreneur / Intrapreneur.
Mark Schnitzer is a skilled professional with a passion for leading teams in creating resilient systems for analyzing complex financial data. He focuses on making data accessible and valuable to a wide range of customers, empowering them to derive insights and make informed decisions.
Mark has a diverse background in transitioning critical systems to the cloud, developing financial modeling tools such as Morgan Stanley ModelWare, and advocating for the utilization of XBRL for financial data analysis. His innovative approach combines technology with business solutions to address industry challenges.
In addition to his technical expertise, Mark excels in team leadership and values diversity and inclusion. He emphasizes talent development and collaboration to drive organizational success. Whether working with startups or assisting enterprises in their digital transformation, Mark is committed to delivering high-quality results.
With a strong foundation in Business Strategy, Leadership, Finance, Data Science, and Engineering, Mark's skill set includes proficiency in SQL, Synapse, Spark, Azure Data Lake, Hadoop, Python, Java, C++, and APL. He has a background in financial modeling, having worked with Morgan Stanley ModelWare and in roles such as Distressed Securities Analyst and Investment Banking Analyst.
Mark Schnitzer holds an MBA from Harvard Business School and a Bachelor's degree in Economics from Columbia College, Columbia University. He has held various leadership positions at Microsoft, such as Partner Director of Software Engineering and General Manager, as well as roles at Morgan Stanley and as the Founder and President of FreeEDGAR.
Highlights
As good as the SEC’s EDGAR and structured data and FFIEC call reports are today, United States financial regulatory data will get even better: more open, more machine readable and less proprietary with the newly drafted rules regarding interoperability of financial regulatory data as part of the Financial Data Transparency Act of 2022.
Identifiers will become more open with the use of Legal Entity Identifier (LEI), Financial Instrument Global Identifier (FIGI), and Financial Services Unique Product Identifier (UPI). Open identifiers will make it easier to move data not just between agencies but also among publishers and users of data by reducing the need to use proprietary and costly identifiers.
There will be broader use of CSV, JSON and XML as data transmission formats, along with HTML with schema conforming data. It is interesting to see PDF/A with reference schema, taxonomy and metadata getting equal billing.
Standardization particularly of taxonomies can get complex and bureaucratic. The agencies are proposing that individual agencies will be able to modify a jointly-established standard taxonomy and use additional taxonomies. This seems like a reasonable approach to avoid the higher overhead of managing joint taxonomies where they are not needed.
I am looking forward to the comment period and ultimately to implementation of the FDTA with all of the benefits of less friction, less cost and faster and broader access to financial data.
Vaqar Pirzada #Kudos Thanks for all of your cross-org support over the years! #TeamPlayer
