COGNNA

Growth Marketing

• **Demand Generation Strategy: **Identify new customer segments and develop multi-channel strategies to drive high-quality traffic to our digital properties.
• **Performance Media Management:** Manage and optimize the advertising budget across Google (Search, Display, YouTube), LinkedIn, and Meta. You’ll be responsible for everything from bid strategy to creative testing.
• **Technical Tracking & Attribution:** Own the implementation of GA4, Google Tag Manager (GTM), and custom conversion tracking to ensure every dollar of spend is accounted for.
• **High-Conversion Landing Pages: **Lead the creation and optimization of campaign-specific landing pages, ensuring the messaging resonates and the user journey is frictionless.
• **Conversion Rate Optimization (CRO): **Run constant A/B tests on copy, design, and forms to maximize the percentage of visitors who convert into leads.
• **ABM & Precision Targeting**: Deploy ABM ads and build Lookalike Audiences to put our brand in front of high-value decision-makers.
• **Data Intelligence & Sales Alignment:** Research prospects and clean data to ensure the sales team is working with the highest quality leads possible.

Growth Marketing Lead

QA Automation Engineer

• **AI Enablement: **Research, pilot, and implement AI tools (LLMs, generative AI agents, and specialized AI platforms) to boost departmental productivity.
• **Workflow Orchestration:** Integrate our tech stack (SaaS tools, internal databases, and APIs) using a mix of custom code (Python) and advanced orchestration platforms (e.g., Make.com, n8n, Zapier).
• **Change Management: **Act as a "Internal Consultant" to train teams on new automated tools and ensure high adoption rates for AI initiatives.
• **Performance Tracking:** Measure and report on time-savings and ROI generated by your automated solutions.
• **Cross-Departmental Automation:** Build and own end-to-end workflows such as:
• **HR:** Automated onboarding/offboarding, smart case creation, and candidate screening flows.
• **Marketing:** AI-assisted content distribution, automated lead enrichment, and performance reporting.
• **Operations:** Intelligent document processing and automated data synchronization across platforms.

Automation Engineer

Data Engineer

• As a Senior Data Engineer, you will be the architect of our security data ecosystem.
• Your primary mission is to design and build high-performance data lake architectures and real-time streaming pipelines that serve as the foundation for COGNNA's Agentic AI initiatives.
• You will ensure that our AI models have access to fresh, high-quality security telemetry through sophisticated ingestion patterns.
• **Key Responsibilities**
• 1. Data Lake & Storage Architecture
• - **Architectural Design:** Design and implement multi-tier Data Lakehouse architectures to support both structured security logs and unstructured AI training data.
• - **Storage Optimization:** Define lifecycle management, partitioning, and clustering strategies to ensure high-performance querying while optimizing for cloud storage costs.
• - **Schema Evolution:** Manage complex schema evolution for security telemetry, ensuring compatibility with downstream AI/ML feature engineering.
• 2. Real-Time & Streaming Processing
• - **Streaming Ingestion:** Build and manage low-latency, high-throughput ingestion pipelines capable of processing millions of security events per second in real-time.
• - **Unified Processing:** Design unified batch and stream processing architectures to ensure consistency across historical analysis and real-time threat detection.
• - **Event-Driven Workflows:** Implement event-driven patterns to trigger AI agent reasoning based on incoming live data streams.
• 3. AI/ML Enablement & Feature Engineering
• - **Vector Data Foundations:** Architect the data infrastructure required to support semantic search applications and variants of RAG architectures for our generative AI models.
• - **Feature Management:** Design and maintain a centralized repository for ML features, ensuring consistent data is used for both model training and real-time inference.
• - **AI Pipeline Orchestration:** Build automated workflows to handle data preparation, model evaluation, and deployment within our cloud AI ecosystem.
• 4. DataOps & Systems Design
• - **Infrastructure as Code:** Utilize declarative tools (e.g., Terraform) to manage the entire lifecycle of our cloud data resources and AI endpoints.
• - **Quality & Observability:** Implement automated data quality frameworks and real-time monitoring to detect "data drift" or pipeline failures before they impact AI model performance.