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AlphaSense is seeking a Data Engineer to build the data layer governing AI risk across the enterprise. This is an analytics engineering role focused on governance data modeling, pipelines, quality, and reporting—not dashboards. You will own the analytical data model for AI governance (systems, agents, owners, risk classifications, assessments, findings, exceptions, control results, incidents, vendors, usage) and the transformation layer that turns raw landed data into that model with tests, version control, and CI.
Key responsibilities include:
**Governance Data Model and Pipelines**: Design and maintain the analytical data model to answer evolving regulatory and board questions. Build transformation layers with version control and CI/CD.
**Data Quality and Lineage**: Own trustworthiness through automated quality checks, freshness and completeness monitoring, and end-to-end lineage tracing any reported figure back to source system, extraction time, and transformation path. Lineage is non-negotiable for audit evidence.
**Registry Data Integrity and AIBOM Reconciliation**: Own scheduled reconciliation between the AI System and Agent Registry and Product Security's product asset and AI bill-of-materials records. Match entities across sources, resolve conflicts, attribute ownership, detect stale records, and produce exception reports.
**Metrics, KPIs, and KRIs**: Design metrics measuring program health—registry coverage, discovery and shadow AI trends, risk tier distribution, assessment throughput, control effectiveness, exception aging, remediation velocity, and service performance. Metrics must survive scrutiny.
**Control Monitoring Analytics**: Own analytics on control check results (pass/fail with evidence): coverage, failure rates and trends, drift detection, time-to-remediation, and reporting to leadership on control effectiveness.
**Executive and Board Reporting**: Build and maintain reporting for the Director, AI Governance Council, CISO, and board with visual and narrative clarity. Be prepared to explain metrics, why they moved, and what decisions they should inform.
**AI Cyber Risk Quantification Data**: Structure risk, incident, control, and exposure data to express AI risk in financial terms for board reporting and investment prioritization. Build the data foundation for risk quantification methodology.
**Audit and Compliance Evidence Analytics**: Own data underpinning ISO 42001 certification, EU AI Act readiness, and internal AI impact assessments. Every figure must be accurate, defensible, traceable, and reproducible on demand.
**Cross-functional Partnership**: Work with Enterprise Data and Analytics on platform placement, semantic definitions, access control, and BI standards. Partner with the AI Security Analyst and Automation Engineer to define data needs and validate reporting reflects ground truth.
You will report to the Director and work alongside an AI Security Analyst and AI Security Automation Engineer. The Automation Engineer owns integration to external source systems and delivers raw data into a landing zone; you own everything downstream: data model, transformation, quality, lineage, metrics, and reporting.
This role aggregates security findings, incidents, control failures, and risk exposure across the enterprise. Discretion, careful handling of sensitive data, and understanding that data access is a reporting obligation are non-negotiable.
**Requirements**
**Foundational Requirements (4+ years required):**
- 4+ years in analytics engineering, data engineering, or BI engineering, with at least some supporting security, risk, compliance, or audit functions
- Strong SQL including window functions, complex joins, and query performance reasoning at scale
- Working Python (pandas or equivalent) for transformation, reconciliation, and analysis
- Data modeling and pipeline design: ETL/ELT, dimensional or equivalent modeling, incremental processing, warehousing concepts
- Hands-on BI and visualization (Tableau, Power BI, Looker, or similar) with judgment about what belongs in charts vs. narrative
- Cloud data platform experience (Snowflake, BigQuery, Redshift, Databricks, or similar)
- Version control and CI/CD for analytics code (Git, dbt, GitHub Actions, or equivalent)
- Comfort with imperfect, incomplete, multi-source data and judgment to know when to reconcile, flag, or refuse to report
- Strong written and visual communication for explaining metrics to decision-makers
**Required Depth (both required; coursework or familiarity insufficient):**
- Metrics consumed by demanding audiences (external auditors, regulators, executive committees, boards) that held up under scrutiny. Be prepared to describe a specific instance where you defended a number, including one where you or the data was wrong and what you did about it.
- Entity reconciliation across conflicting systems of record with incomplete, contradictory, or decaying data. Experience with identity resolution, ownership attribution, stale records, and resolving disagreements between authoritative systems.
**Strongly Preferred:**
- Experience building metrics or dashboards for security operations, GRC, vendor risk, or audit programs
- API-based data ingestion from security and identity tooling (SIEM, CASB, IAM, EDR, cloud audit logs)
- Data quality and observability tooling, lineage or catalog implementation
- Exposure to AI governance frameworks (ISO 42001, NIST AI RMF, EU AI Act risk tiers) or security frameworks (NIST CSF, SOC 2), ideally through supplying audit evidence
- Familiarity with cyber risk quantification methodologies (FAIR) or board-level risk reporting experience
- Exposure to AI asset inventory, AIBOM concepts, agent registries, or non-human identity data
- Experience supporting IPO readiness, SOX, or investor due diligence from data and reporting perspective
