At a glance
- Experience
- 4+ years experience
- Location
- Bengaluru · Pune
About the role
- AlphaSense is building the function that governs AI across the enterprise, covering products, engineering workflows, business processes, third-party services, and autonomous agents.
- You will build the data layer that makes defensible, traceable answers possible for auditors, the AI Governance Council, the CISO, and the board.
- This is an analytics engineering role where you will own the governance data model, the pipelines that populate it, data quality and lineage, and the metrics and reporting built on top.
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 the trustworthiness of governance data by building automated data quality checks, freshness and completeness monitoring, and end-to-end lineage.
- Own the scheduled reconciliation between systems of record, matching entities across sources, resolving conflicts, attributing ownership, and producing exception reports.
- Design and own the metric set measuring registry coverage, discovery and shadow AI trends, risk tier distribution, assessment throughput and cycle time, control effectiveness and drift, exception aging, remediation velocity, and service performance.
- Build and maintain analytics on top of control checks to monitor coverage, failure rates, trends, drift detection, time-to-remediation, and reporting.
- Build and maintain reporting for the Director, the AI Governance Council, the CISO, and the board with visual and narrative clarity.
- Structure risk, incident, control, and exposure data so AI risk can be expressed in financial terms for board reporting and investment prioritization.
- Own the data underpinning ISO 42001 certification, EU AI Act readiness, and internal AI impact assessments to ensure figures are accurate, defensible, traceable, and reproducible.
- Partner cross-functionally with Enterprise Data and Analytics, Product Security, SecOps, Identity, Secure IT, and Procurement to align on platform placement, definitions, access control, and BI standards.
Requirements
- 4+ years in analytics engineering, data engineering, or BI engineering, with at least some of it supporting security, risk, compliance, or audit functions.
- Strong SQL, including window functions, complex joins, and reasoning about query performance at scale.
- Working Python (pandas or equivalent) for transformation, reconciliation, and analysis.
- Data modeling and pipeline design experience including ETL/ELT, dimensional or equivalent modeling, incremental processing, and warehousing concepts.
- Hands-on BI and visualization work (Tableau, Power BI, Looker, or similar) with judgment about 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).
- Demonstrated applied experience building metrics consumed by a demanding audience that held up under scrutiny from an external auditor, regulator, executive committee, or board.
- Demonstrated applied experience in entity reconciliation across conflicting systems of record 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 (SIEM, CASB, IAM, EDR, cloud audit logs).
- Data quality and observability tooling, and 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).
- Familiarity with cyber risk quantification methodologies such as FAIR, or experience 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.
Workplace
- Office locations include Bengaluru and Pune.
Skills
- SQL
- Python
- Pandas
- ETL
- ELT
- Tableau
- Power BI
- Looker
- Snowflake
- BigQuery
- Redshift
- Databricks
Show all 15 skills
- Git
- Dbt
- GitHub Actions