We are looking for a Lead Data Security Solution Engineer to examine requirements created by the Business Analyst and design, validate, and prototype secure technical solutions for BR Data In-Licensing. The environment is largely relational, with primary enforcement at the data and database layer, focusing on identity-based authentication, fine-grained authorization, data firewalling, auditability, lifecycle control, and defensible deletion. Responsibilities Examine Data In-Licensing requirements, workflow maps, data flows, entitlement rules, and dependency records; evaluate relational database platforms, schemas, connectivity methods, query patterns, and pinpoint where licensed data is stored, transformed, replicated, extracted, or backed up Evaluate current database security capabilities and pinpoint gaps, constraints, risks, and prerequisites Design authentication for named users, service identities, workloads, and analytical tools connecting to databases; assess OAuth/OIDC or token-based authentication; integrate enterprise identities, security groups, and database principals; minimize reliance on shared or long-lived credentials Build database-level controls limiting access to authorized users, groups, services, or licensing populations; apply row-level security, column-level security, dynamic masking, views, or policy-based filtering; ensure controls are enforced at the data layer, not solely dependent on application logic Create database auditing that captures who accessed data, what, when, and which identity was used; define logging for queries, exports, failed attempts, and permission changes; integrate audit telemetry with monitoring and reporting; make audit records searchable, protected, and useful for investigation Build controls for access revocation when users change roles or licensing rights expire; design methods to locate and remove licensed data from primary stores and downstream copies; address deletion implications for backups, archives, replication, and referential integrity; define validation and evidence mechanisms Convert requirements into architectures, technical specifications, and implementation plans; compare solution options across security, feasibility, performance, scalability, and cost; partner with database, IAM, security architecture, infrastructure, application, and governance teams Choose database and workflow candidates for POC; define POC architecture, test data, success criteria, and rollback approach; implement and validate authentication, fine-grained authorization, auditing, revocation, and deletion scenarios; document outcomes, limitations, and recommendations Requirements 5+ years of relevant experience, including at least 1 year in a leadership role Hands-on background in relational databases, SQL, and database architecture, including schemas, roles, privileges, and access paths Practical expertise in database-level authorization, including row-level and column-level controls or fine-grained mechanisms Strong knowledge of database authentication, IAM, OAuth 2.0, OIDC, tokens, service identities, workload identities, and secrets management Background in designing secure database connectivity and integrations for applications, analytical tools, APIs, ETL/ELT, and data pipelines Proficiency in database auditing, query/activity monitoring, centralized logging, alerting, and investigation support Understanding of data lineage, lifecycle, retention, revocation, deletion, backup, archive, and recovery implications Capability to assess heterogeneous database technologies and design platform-appropriate controls Skills in architecture, engineering, troubleshooting, documentation, and stakeholder collaboration English proficiency at an Upper-Intermediate level (B2) or higher Nice to have Familiarity with enterprise data platforms, cloud or hybrid databases, data catalogs, access governance, and policy-enforcement technologies Background in pharmaceutical research, scientific data, intellectual property protection, data licensing, or regulated environments Expertise in designing and running security POCs and turning validated patterns into production-ready designs Knowledge of privacy, contractual-use restrictions, segregation, and evidence requirements that apply to licensed data
