Security Engineering Manager

Location: Boston, MA - 3 days/week (onsite)

Duration: Long Term

Experience: 14+ Yrs

Interview Mode: In‑person

USC / GC Only

Only Local to Massachusetts

This is a unique opportunity for a strong technologist to be a founding member of the team building a strategic data and AI platform from scratch for a well‑established bank. The Security Engineering Manager will lead and execute cybersecurity engineering across existing on‑premises infrastructure, new AWS cloud environment, Snowflake data platform, and emerging AI application ecosystem. The role is not a pure oversight or policy position;

it requires a senior technical security practitioner who can hands‑on design, build, harden, implement, troubleshoot, and continuously improve security controls while managing and mentoring a small team.

• Security Engineering & Architecture
  • Own the design, implementation, and continuous improvement of security controls across infrastructure, cloud, applications, data platforms, and AI solutions.
  • Design and implement practical security architectures for on‑premises systems, AWS, Snowflake, and internally developed AI applications.
  • Translate cybersecurity standards and risk requirements into deployable technical controls.
  • Build secure‑by‑design patterns for identity, network segmentation, encryption, logging, monitoring, endpoint protection, vulnerability management, and access governance.
  • Serve as a senior technical security advisor to infrastructure, engineering, data, AI, and vendor teams.
  • Evaluate new technologies and embed security requirements early in design and delivery.
  • Own AWS security responsibilities across identity, networking, data protection, monitoring, governance, and workload security.
• New AWS Environment Design
  • Secure a new AWS environment from initial design through operationalization.
  • Implement multi‑account security patterns, IAM controls, least privilege access, Service Control Policies (SCPs), logging, monitoring, encryption, secrets management, vulnerability scanning, and network segmentation.
  • Design secure VPC, subnet, routing, security group, and Network ACL patterns.
  • Apply controls across services such as IAM, Organizations, Cloud Trail, Cloud Watch, Guard Duty, Security Hub, Config, KMS, Secrets Manager, Macie, Inspector, S3, Lambda, RDS, EC2, ECS/EKS as applicable.
  • Partner with infrastructure and engineering teams to embed security into CI/CD, IaC, cloud provisioning, and operational support.
  • Establish AWS security baselines and exception management processes.
• Existing Data Center and Infrastructure
  • Maintain and improve controls across servers, endpoints, firewalls, networks, Active Directory, privileged access, remote access, vulnerability management, patching, EDR, SIEM/logging, backup security, and segmentation.
  • Strengthen identity and access controls across Microsoft/Windows environments.
  • Support remediation of audit findings and security gaps across existing infrastructure.
  • Partner with IT operations to ensure cybersecurity controls are practical, sustainable, and operationally reliable.
  • Modernize legacy security patterns while reducing operational burden.
• Snowflake & Data Platform Security
  • Design and review Snowflake access controls, role hierarchy, authentication, MFA/SSO integration, network policies, data classification, masking, row‑level access, and audit logging.
  • Partner with data engineering and analytics teams to protect sensitive banking data appropriately.
  • Support security patterns for data ingestion, transformation, sharing, and reporting.
  • Ensure monitoring, alerting, and governance around privileged access, service accounts, and data movement.
  • Snowflake experience is strongly preferred; direct implementation experience is a major plus.
• AI Application Security
  • Define and implement security guardrails for AI applications (RAG, chatbots, AI agents, document intelligence, and internal AI tools).
  • Address AI‑specific risks such as prompt injection, data leakage, insecure output handling, excessive agency, model misuse, unsafe integrations, and sensitive information exposure.
  • Partner with AI/data engineering teams to secure model access, data flows, vector stores,…