At a glance
- Experience
- 4+ years experience
- Location
- Pune
About the role
- AlphaSense is building the function that governs AI across the enterprise and is looking for a Data Engineer to build the data layer that makes defensible, traceable AI risk reporting possible.
- This is an analytics engineering role where you will own the governance data model, transformation pipelines, data quality and lineage, and executive-level reporting.
Responsibilities
- Own the analytical data model for AI governance including 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 the governance model with tests, version control, and CI.
- Own data trustworthiness by building automated data quality checks, freshness and completeness monitoring, and end-to-end lineage tracing figures back to source systems.
- Perform scheduled reconciliation between authoritative enterprise systems, matching entities, resolving conflicts, attributing ownership, and producing exception reports.
- Design and own metrics, KPIs, and KRIs including registry coverage, discovery and shadow AI trends, risk tier distribution, assessment throughput, and control effectiveness.
- Own analytics on control check results covering control set coverage, failure rates, drift detection, and time-to-remediation reporting.
- Build and maintain executive, council, and board reporting with visual and narrative clarity.
- Structure risk, incident, control, and exposure data to enable AI cyber risk quantification for board reporting and investment prioritization.
- Own data underpinning ISO 42001 certification, EU AI Act readiness, and internal AI impact assessments for audit and compliance evidence analytics.
- Partner cross-functionally with Enterprise Data and Analytics, Product Security, SecOps, Identity, Secure IT, and Procurement to align on semantic definitions and ground truth.
Requirements
- 4+ years in analytics engineering, data engineering, or BI engineering, with at least some supporting security, risk, compliance, or audit functions.
- Strong SQL skills including window functions, complex joins, and reasoning about query performance at scale.
- Working Python experience using pandas or equivalent for transformation, reconciliation, and analysis.
- Experience with data modeling and pipeline design including ETL/ELT, dimensional or equivalent modeling, incremental processing, and warehousing concepts.
- Hands-on BI and visualization work using tools such as Tableau, Power BI, or Looker.
- Cloud data platform experience such as Snowflake, BigQuery, Redshift, or Databricks.
- Version control and CI/CD for analytics code using Git, dbt, GitHub Actions, or equivalent.
- Demonstrated applied experience building metrics consumed by a demanding audience that were successfully defended before an external auditor, regulator, or board.
- Demonstrated applied experience in entity reconciliation across conflicting systems of record dealing with incomplete, contradictory, or decaying data.
Preferred qualifications
- Experience building metrics or dashboards for security operations, GRC, vendor risk, or audit programs.
- API-based data ingestion from security and identity tooling including SIEM, CASB, IAM, EDR, and cloud audit logs.
- Data quality and observability tooling, and lineage or catalog implementation.
- Exposure to AI governance frameworks like ISO 42001, NIST AI RMF, or EU AI Act risk tiers, or security frameworks like NIST CSF or SOC 2.
- Familiarity with cyber risk quantification methodologies such as FAIR or supporting board-level risk reporting.
- Exposure to AI asset inventory and AIBOM concepts, agent registries, or non-human identity data.
- Experience supporting IPO readiness, SOX, or investor due diligence from a data and reporting perspective.
Skills
- SQL
- Python
- Pandas
- ETL
- ELT
- Tableau
- Power BI
- Looker
- Snowflake
- BigQuery
- Redshift
- Databricks
Show all 14 skills
- Git
- Dbt