SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
AlphaSense is seeking a Data Engineer to build the data layer that governs AI risk across the enterprise. This is an analytics engineering role focused on creating defensible, auditable data foundations for AI governance reporting to the board, CISO, and external auditors.
You will own the governance data model, transformation pipelines, data quality, lineage, and metrics that answer critical questions about AI systems, risk classifications, control effectiveness, and compliance posture. Your work will support ISO 42001 certification, EU AI Act readiness, and internal AI impact assessments.
Key responsibilities include:
- Building and maintaining the analytical data model for AI governance (systems, agents, owners, risk classifications, assessments, findings, exceptions, control results, incidents, vendors, usage)
- Implementing automated data quality checks, freshness monitoring, and end-to-end lineage that traces reported figures back to source systems
- Owning scheduled reconciliation between the AI System Registry and Product Security's asset/AIBOM records, including identity resolution, ownership attribution, and conflict resolution
- Designing and owning metrics and KPIs that measure program health: registry coverage, risk distribution, assessment throughput, control effectiveness, remediation velocity
- Building control monitoring analytics on pass/fail results from automated checks, including coverage, failure rates, drift detection, and time-to-remediation
- Creating executive, council, and board reporting with visual and narrative clarity
- Structuring risk and incident data to enable cyber risk quantification for board reporting and investment prioritization
- Partnering with Enterprise Data and Analytics on platform placement, semantic definitions, and BI standards
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 raw data delivery; you own everything downstream: data model, transformation, quality, lineage, metrics, and reporting.
Requirements:
- 4+ years in analytics engineering, data engineering, or BI engineering, with experience 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 expertise: ETL/ELT, dimensional modeling, incremental processing, warehousing concepts
- Hands-on BI and visualization work (Tableau, Power BI, Looker, or similar) with judgment about data presentation
- 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 about when to reconcile, flag, or refuse to report
- Strong written and visual communication skills
- Demonstrated experience building metrics consumed by demanding audiences (external auditors, regulators, executives, boards) and defending those numbers under scrutiny
- Demonstrated experience with entity reconciliation across conflicting systems of record, including identity resolution, ownership attribution, and handling contradictory data
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) or security frameworks (NIST CSF, SOC 2) through audit evidence work
- Familiarity with cyber risk quantification methodologies (FAIR) or board-level risk reporting
- Exposure to AI asset inventory, AIBOM concepts, agent registries, or non-human identity data
- Experience supporting IPO readiness, SOX, or investor due diligence from a data perspective
Critical requirement: 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.
