Senior Security Data Engineer, Vulnerability Risk

BioSpace; Cambridge (MA); On-site; USD 131,000 - 209,000; Full time

Benefits offered by this job

Healthcare

Well-being resources

Family planning benefits

Paid time off

Year-end shutdown

Job summary

Moderna is seeking a Senior Security Data Engineer, Vulnerability Risk Management to own and scale data products across the full lifecycle—from raw source ingestion to normalized evidence, risk model outputs, and operational reporting.

You will design robust ingestion pipelines, model-ready features, and data quality controls, enabling remediation teams and leadership with trusted insights in a fast-paced, highly collaborative biotech environment.

Qualifications

• 5+ years of experience in data engineering, analytics engineering, security data engineering, or related technical disciplines.
• Strong hands-on proficiency with Python and SQL in production data pipeline environments.
• Experience designing, building, and operating ETL/ELT pipelines across heterogeneous source systems.
• Experience with data modeling, schema evolution, incremental processing, and data quality controls.
• Ability to work with messy, incomplete, or inconsistent datasets and make pragmatic normalization decisions.
• Experience supporting data products from source ingestion through reporting, analytics, model inputs, or decision-support outputs.
• Experience designing data models that support ownership routing, triage, workflow state, or operational decision-making.
• Strong analytical, troubleshooting, documentation, and communication skills.

Responsibilities

• Design, build, and operate reliable ingestion pipelines for asset, vulnerability, findings, remediation, threat intelligence, and business-context data sources.
• Build reusable ingestion patterns that can absorb new sources without brittle, one-off logic.
• Support batch and incremental processing patterns from raw source capture through normalized, model-ready data products.
• Partner with source-system owners to clarify schema changes, source freshness, field semantics, and operational constraints.
• Own transformations from raw evidence into normalized security entities such as assets, vulnerabilities, findings, owners, exposure context, risk-reduction actions, and validation states.
• Engineer model-ready features that encode asset ownership, exposure, business importance, exploitability, remediation state, and organizational context.
• Support risk model outputs and operational reporting datasets used by remediation owners and leadership.
• Model ownership and accountability signals across code, artifacts, platforms, infrastructure, and application contexts to reduce ambiguity in remediation routing.
• Make pragmatic normalization decisions for messy, heterogeneous data while documenting assumptions, confidence, and tradeoffs.
• Adapt vulnerability detection and triage data flows to support increasing AI-driven finding volume without losing ownership, severity, or remediation context.
• Build data foundations for scanning workflows across code repositories, build artifacts, deployed platforms, and runtime environments.
• Connect vulnerability signals to remediation workflow needs, including owner routing, action state, validation evidence, exception paths, and closure reporting.
• Partner with security, engineering, and platform teams to unify how findings are represented from discovery through remediation.
• Implement data quality checks for schema validity, completeness, freshness, duplication, drift, and unexpected distribution changes.
• Maintain lineage documentation so risk scores, reports, and owner views can be traced back to source inputs.
• Build monitoring, alerting, and runbooks for data pipeline reliability and source degradation.
• Partner with downstream users to resolve data defects and improve trust in pipeline outputs.
• Prepare reliable, pre-aggregated data products for dashboards, operational views, risk reporting, and experimentation.
• Collaborate with partners to translate new questions into durable data features.
• Improve data contracts, documentation, and self-service access patterns for technical and non-technical stakeholders.
• Continuously refine feature hypotheses and data products as the vulnerability risk model and remediation operating model mature.

Skills

Python

SQL

ETL/ELT

Data modeling

Data quality

Data products

Ownership routing

Job description

Moderna is seeking a Senior Security Data Engineer, Vulnerability Risk Management to own and scale data products across the full lifecycle—from raw source ingestion to normalized evidence, risk model outputs, and operational reporting.

You will design robust ingestion pipelines, model-ready features, and data quality controls, enabling remediation teams and leadership with trusted insights in a fast-paced, highly collaborative biotech environment.