via99 · Somerville, Massachusetts, United States, Washington, District of Columbia, United States
Skills: Python, SQL, AWS
VIA is making an impact, and so can you. Headquartered in Boston, VIA provides mission-critical software for secure data exchange and trustworthy AI without compromising speed, simplicity, or resilience. Its military-grade architecture helps defense, public safety agencies, and humanitarian organizations use AI while maintaining control of sensitive, distributed data even in contested or offline environments. Learn more: solvewithvia.com . An impressive mission requires an equally impressive Senior Data Analytics Engineer. As a Senior Data Analytics Engineer at VIA, you'll play a pivotal role in the growth of our solutions. You'll turn raw, complex, and often ambiguous data into the clear, trusted narratives that power VIA's data products and guide our customers' decisions. Operating on a high-velocity Agile team, you'll work cross-functionally with software engineers, data peers, and client delivery professionals to lead data initiatives end to end, in an environment where precision, security, and clarity are non-negotiable. In this role, you will: Understand the data and the domain Partner with VIA's client delivery team and customers to translate domain knowledge into data infrastructure requirements, validate assumptions, and resolve data-related issues Explore customer data to build a clear picture of contents and characteristics (e.g. averages, expected ranges, trends, standard deviations) and make suggestions for data cleaning and analysis Build AI-powered data products Deliver data-based products to external customers, including interactive data analysis and investigation platforms, data quality reports, statistical analysis, and visualizations that turn complex findings into clear stories Build AI into VIA's data products, for example through automated insights, anomaly detection, AI-assisted data quality checks, and natural-language interfaces over operational data Evaluate the quality and reliability of AI/ML outputs against domain expectations, and desig…
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