THE ROLE 

The Senior Application Security Engineer is a key member of Cognism’s Information Security Team, reporting into the Application & Infrastructure Security Manager. Your mission is to embed security by design across our engineering and product organization by integrating modern application security practices throughout the full development lifecycle. We focus on building secure, scalable, and resilient systems while enabling the business to innovate quickly and safely. It is to drive a culture where the fastest path for our engineers is the securest path. 

You will work closely with Product, Engineering, Architecture, and Data teams to understand risks within our platform, including risks introduced by AI powered features, and ensure the right controls, guardrails, and security patterns are built into the product at its inception – all the while ensuring a balanced approach to the product experience that our thousands of large global enterprise customers use every day. 

This role is ideal for a senior IC who is technical, collaborative, and pragmatic, with the ability to influence engineering teams while driving hands on improvements to Cognism’s secure SDLC. 

KEY RESPONSIBILITIES 

Security by Design & Product Integration 

• Partner with Product, Web and Data Engineering teams from the ideation stage to ensure security requirements are considered early in feature and model design.
• Translate product and application risks into actionable security controls, making recommendations repeatable to build guardrails and guidance that product, design, and engineering teams can apply as they scale and build the products in the teams they own.
• Help shape security acceptance criteria and guide engineering teams during design reviews and backlog planning. 

Application Risk Assessment & AI Security 

• Identify and assess application risks across Cognism’s SaaS platform, data processing pipelines, including emerging risks associated with AI/ML capabilities.
• Contribute to AI feature reviews and participate in AI risk assessments to ensure responsible and secure use of models, in a way that balances the need to innovate and challenge the status quo in AI implementation.
• Assess and pragmatically recommend mitigations for security risks in data pipelines, model-training workflows, feature stores, and ML systems, ensuring strong controls for data access, data lineage, model integrity, and protection of sensitive datasets. 

Secure SDLC & DevSecOps Enablement 

• Partner with our engineering and platform team to embed security guardrails into our software development lifecycle and agile engineering workflows.
• Partner with engineering teams to integrate and optimize security tooling into CI/CD pipelines (SAST, SCA, DAST, container scanning, IaC scanning).
• Drive automation and developer-friendly security processes that minimize friction and support rapid delivery. 

Threat Modelling & Architecture Support 

• Conduct and facilitate a self-serve, risk-driven approach to pragmatic threat modelling sessions for new features, services, and AI components.
• Conduct threat modelling for data architectures, including ingestion, transformation, storage, streaming, and ML model deployment patterns, ensuring data confidentiality, integrity, and responsible use.
• Provide hands-on application security guidance to engineering teams, helping them implement secure APIs, microservices, data flows, and integrations.
• Maintain and expand Cognism’s secure coding standards, guidance, and reusable security patterns. 

Application Security Testing & Engineering 

• Perform hands-on security testing (manual and automated) for web applications, microservices, APIs, and cloud components.
• Plan, coordinate, and oversee penetration tests, red team exercises, and third-party security assessments, ensuring findings are addressed and tracked.
• Validate findings, assist with prioritization, and partner with engineering teams on remediation strategies. 

Collaboration, Education & Influence 

• Work directly with product squads, acting as a trusted advisor and embedded security partner. Take the time to understand what other teams are working on, what business priorities are, and partner with teams to recommend risk mitigations that balance risk with opportunity and that take into account the threat landscape. 
• Deliver security training, workshops, and guidance to improve engineering teams’ security maturity.Communicate security risk and tradeoffs clearly and constructively to technical and nontechnical stakeholders. 

CORE COMPETENCIES 

• Strong technical depth in application security, cloud security, and secure development.
• Understanding of modern data stack components (e.g., data pipelines, feature stores) and the ability to collaborate effectively with data practitioners.
• Deep understanding of communication protocols used for web development is a must-have.
• Hands on experience with web application development, specifically for back-end development is a must-have competency. 
• Risk-Based Prioritization: Ability to distinguish between theoretical security risks and actual business threats, demonstrating a "risk-driven" rather than "compliance-driven" mindset. Comfortable balancing security risk with product and commercial realities.
• Contextual Communication: Capacity to translate complex technical vulnerabilities into business-impact stories that resonate with non-technical stakeholders and product owners. 
• Collaborative Conflict Resolution: Proven track record of approaching engineering friction with transparency and pragmatism, balancing a firm stance on security with a deep care for the developer experience. Pragmatic problem solver with a growth mindset and bias toward action.
• Architectural Empathy: Ability to put yourself in the position of your users (the engineers) to build security guardrails that are repeatable and embedded into existing workflows rather than added as hurdles. 

EDUCATION & EXPERIENCE 

• 5–10+ years of experience in Application Security, Product Security, or Security Engineering roles.
• Strong experience securing cloud native SaaS platforms (AWS preferred) and Data pipelines. 
• Handson experience with secure coding, application testing, and CI/CD security automation.
• Experience working closely with engineering and product teams in agile environments.
• Familiarity with security frameworks such as OWASP ASVS, OWASP Top 10, NIST, ISO 27001/2.
• Experience evaluating and securing AI/ML enabled features is a strong advantage.
• Experience in SME or high-growth SaaS environments preferred.

Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters.

You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
• Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
• Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
• Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
• Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
• Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
• Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
• Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
• Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery

We're looking for:

• Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
• Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
• Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
• A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
• Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
• Excellent communication skills with a pragmatic, problem-solving approach

Bonus points for:

• Experience with orchestration and workflow tooling (Airflow or similar)
• Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
• Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
• Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
• Familiarity with streaming/event-heavy ecosystems (Kafka or similar)

Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters.

You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
• Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
• Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
• Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
• Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
• Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
• Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
• Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
• Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery

We're looking for:

• Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
• Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
• Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
• A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
• Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
• Excellent communication skills with a pragmatic, problem-solving approach

Bonus points for:

• Experience with orchestration and workflow tooling (Airflow or similar)
• Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
• Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
• Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
• Familiarity with streaming/event-heavy ecosystems (Kafka or similar)

About the Role

Superbet is building the future of gaming experiences, and we need a visionary lead to architect our AI-first UX Research Operations function from the ground up. This is a rare opportunity to define how AI transforms research at scale - moving from traditional qual/quant methods to intelligent, automated insight generation that keeps pace with our global expansion. 

You'll be the bridge between cutting-edge AI capabilities and human-centered research excellence, building systems that allow our product teams to make confident, data-informed decisions 10x faster than traditional research allows. 

This is not a traditional ResearchOps role. We're looking for someone who sees AI as the primary research infrastructure, not just a productivity tool. 

You bring methodological mastery, creative curiosity, and the kind of presence that makes participants forget they're being observed and leaders believe you shape company wide narratives. You also bring a future positive mindset: you are actively integrating AI tools into your workflow to do more, faster, without sacrificing the depth that makes qual irreplaceable. You know when AI is helpful and when it is not and can help junior researchers make decisions around when to use them. 

What You Will Build

Year 1: Foundation

• AI Research Infrastructure: Design and implement an AI-native research stack that automates insight synthesis, pattern detection, and continuous discovery 
• Intelligent Repository: Build a living research knowledge base where AI surfaces relevant insights the moment teams need them—no searching through Notion or Dovetail 
• Automated Research Workflows: Deploy AI agents for interview transcription, analysis, synthesis, competitive intelligence, and sentiment monitoring across 12+ markets 
• Research Democratization: Enable product managers, designers, and engineers to self-serve 80% of research questions through AI-assisted tools 

Year 2: Scale 

• Continuous User Understanding: Establish always-on research systems (AI-powered surveys, behavioral analytics, community listening) that generate insights without manual studies Predictive Research: Build models that anticipate user needs, friction points, and opportunities before traditional research would surface them 
• Cross-Market Intelligence: Create AI systems that synthesize insights across Superbet's global markets, detecting regional patterns and universal truths 
• Research Quality Assurance: Implement AI-assisted validation frameworks to ensure insights are reliable, unbiased, and actionable

Year 3: Always On Insights 

• AI predicts user friction points before traditional research surfaces them 
• Cross-market insight synthesis happens automatically 
• Research velocity 10x faster than traditional methods 
• Industry-leading case study: "How Superbet built AI-first research" 

What Success Looks Like 

6 Months:

• - AI research stack operational across 3+ product teams 
• - 50% reduction in time-to-insight for common research questions 
• - Research knowledge base with 100+ synthesized insights, AI-searchable 

12 Months: - Product teams self-serve 70%+ of research needs through AI tools - Continuous user understanding systems running across all markets - Research influences 90%+ of major product decisions 

What We Are Looking For

Basic Experience

• Knowledge of Excel (Pivot Tables, VLookUps) at an advanced level and SQL
• Experience working cross functionally with tech and non-tech teams
• Experience defining program requirements and using data and metrics to determine improvements
• Experience in program or project management
• Experience implementing repeatable processes and driving automation or standardization
• Experience defining and executing program requirements within strict timelines

Essential Experience

ResearchOps Excellence (Critical) - Built research operations from 0→1 at a high-growth company - Designed research repositories, tooling stacks, and democratization programs- Managed research panels, participant recruitment, and compliance frameworks- Scaled research to support 50+ stakeholders across multiple product teams AI & Research Intersection (Critical) 

• - 5+ years in UX Research, with 2+ years actively using AI in research workflows
• - Shipped AI-powered research tools, processes, or platforms (not just "used ChatGPT") 
• - Deep understanding of LLMs, RAG systems, prompt engineering for research applications 
• - Experience evaluating AI research tools and building AI-augmented research practices 
• Technical Acumen (Critical) 
• - Comfortable reading code, writing SQL, or working with APIs 
• - Built custom AI workflows (GPT API, Claude API, Zapier, Make) 

Industry Context (Preferred) - Experience in regulated industries (gambling, fintech, healthcare)- Multi-market or international research experience - Background in high-velocity product environments (weekly releases) 

Skills & Mindset

• World-class operator: You have a gift for going across the organization to find the most optimal ways of work - time, money, resource wise.
• AI-enhanced, not AI-dependent: You use AI to go faster and deeper, but your analytical judgement is irreplaceable and you know it. You apply AI tools critically, not uncritically.
• Meticulous planner: You are an expert in Operations and constantly keep improving the team’s delivery model.  
• Org Mover: Your research is not theory alone, you know how to make things tangible, bite sized and move people and work forward. 
• Embrace difficult conversations - Driving research impact is as much about doing good research as it is about convincing people to see things your way. You work with people both easy and difficult to collaborate with to make great experiences come alive. 
• Frugal: You continuously improve our spending habits and drive financial efficiency for the org. 

Nice to Have

• Experience in high intensity consumer products, marketplaces, gaming, financial services, with some degree of experience in trust and safety is useful. 
• Experience in launching new programs, markets and changing large scale work processes in short turn around.
• Experience in some European languages. 

Why This Role at Superbet

Sports is one of the richest contexts for qualitative research: the experience is emotionally intense, socially embedded, culturally specific, and morally complex. Users have contradictory motivations. Their stated and actual behaviour diverge. The moments of joy and frustration are extreme. Understanding all of this deeply, not just what users click, but why they feel what they feel - is what separates good product decisions from great ones.

• Access to a diverse, passionate user base across 12+ markets - every cultural context brings new research challenges.
• A team that values qualitative rigour and won't ask you to reduce everything to a number.
• Senior visibility: your insights will be read by the CPO and product leadership, not just deposited in a repo.
• A Principal Quant Researcher as a genuine peer - someone to build with, argue with, and create better mixed-methods evidence with.
• Competitive compensation at Staff IC level with equity and a genuine investment in your development as a researcher.