Data Engineer

<div class="content-intro"><h2><strong>About AlphaSense: </strong></h2> <p style="text-align: justify;"><span style="font-size: 12pt;">The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. </span></p> <p style="text-align: justify;"><span style="font-size: 12pt;">The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us!</span></p></div><h3>About the Role</h3> <p>AlphaSense is building the function that governs AI across the enterprise: the product, engineering workflows, business processes, third-party services, and a fast-growing footprint of autonomous agents and MCP connectors. That function has one hard obligation it cannot delegate. When the board asks how much AI risk we carry, or an ISO 42001 auditor asks where a control-coverage number came from, there has to be a defensible answer with a traceable path back to a source system.</p> <p>You will build the data layer that makes that answer possible.</p> <p>This is an analytics engineering role, not a dashboard role. You will own the governance data model, the pipelines that populate it, the data quality and lineage that make it trustworthy, and the metrics and reporting built on top. The numbers you produce will go to the CISO, the AI Governance Council, external auditors, and the board. Some of them will be challenged. Your job is to make sure they hold up.</p> <p>You will work alongside an AI Security Analyst and an AI Security Automation Engineer, reporting to the Director. You will also work with Enterprise Data and Analytics on platform, semantic definitions, and BI standards, and with Product Security, SecOps, Identity, Secure IT, and Procurement, whose systems supply most of your data.</p> <p><strong>Scope boundary, stated up front.</strong> The Automation Engineer owns integration to external source systems and delivers raw data into a defined landing zone. You own everything downstream of that boundary: the data model, transformation, quality, lineage, metrics, and reporting. You specify what you need and in what shape; you are not maintaining seven partner API integrations yourself.</p> <h3>What You'll Own</h3> <ul> <li><strong>Governance Data Model and Pipelines<br></strong>Own the analytical data model for AI governance: AI systems and agents, owners, risk classifications, assessments, findings, exceptions, control results, incidents, vendors, and usage. Build and maintain the transformation layer that turns raw landed data into that model, with tests, version control, and CI.<br>The model has to answer questions nobody has asked yet, because the regulatory and board questions will keep changing. Design for that.</li> </ul> <ul> <li><strong>Data Quality and Lineage</strong><br>Own the trustworthiness of governance data. Bui