Senior Risk & Compliance Engineer - Data

Instacart

28d•CA$153,000 - CA$187,000•Remote

About The Position

Instacart's Governance, Risk and Compliance (GRC) team is seeking a Senior Risk & Compliance Engineer with data science expertise to help build an automated, engineering-grade risk program. This role involves writing production-level code, developing signal ingestion pipelines, and creating probabilistic risk models to provide a quantified view of the company's risk posture. The goal is to protect Instacart's customers and products at scale by transforming a manual, reactive discipline into a data-driven, automated program. This position offers the opportunity to provide value across security, engineering, and executive stakeholders.

Requirements

5+ years of experience in data engineering, with demonstrated ability to write production-level code in Python and SQL (PostgreSQL, Presto, or SparkSQL).
3+ years of experience building and deploying machine learning or probabilistic models (e.g., Bayesian models) in a production environment.
Experience building data pipelines that ingest real-time or near-real-time data across multiple formats, handling both stream and batch processing at scale.
Experience with data modeling for classification, normalization, and risk or anomaly detection signal development.
Experience developing metrics that inform security and business decisions.

Nice To Haves

Familiarity with security risk concepts including threat intelligence enrichment pipelines, EPSS, or CISA KEV.
Familiarity with quantitative risk frameworks such as FAIR.
Exposure to security frameworks such as NIST CSF, SOC 2 as context for what controls the data is measuring.
Demonstrated ability to translate risk model outputs into executive or board-level narratives.
A genuine passion for building systems that protect customers and products, and a track record of operating effectively in fast-paced, ambiguous environments where the program is still being shaped.

Responsibilities

Build automated signal ingestion pipelines that pull real-time data from security tooling, normalizing, enriching, and scoring raw findings into actionable, ranked risk intelligence that drives remediation decisions across the organization.
Develop probabilistic risk models that express security exposure as probability distributions, giving leadership a quantified, confidence-backed view of breach likelihood and expected losses, connecting model outputs directly to investment decisions and board-level reporting.
Identify systemic choke points across the attack surface, high-leverage remediation paths where a single fix eliminates risk at scale, and prioritize them by expected impact to maximize the efficiency of our security program.
Build dashboards and data-driven insights that cascade risk visibility across security, engineering, and executive stakeholders, translating complex model outputs into language that resonates at every level of the organization.
Support risk quantification efforts that express security exposure in financial terms, connecting model outputs to investment decisions and board-level reporting.