Top Tech Jobs & Startup Jobs

Develop and optimize production-grade PySpark applications for distributed data processing. Responsibilities include implementing transformations, joins, aggregations, window functions, deduplication, and incremental processing; diagnosing Spark performance issues; optimizing partitions, joins, shuffles, and execution plans; and maintaining reliable batch pipelines. The role owns engineering problems through implementation, testing, deployment, troubleshooting, documentation, and collaboration with technical stakeholders.

Develops and owns production-grade PySpark and Apache Spark applications for batch and incremental data processing. Responsibilities include implementing transformations, joins, aggregations, window functions, and deduplication; diagnosing Spark execution and performance issues; optimizing partitions, joins, shuffles, and resource usage; troubleshooting production failures; and delivering reliable, tested, maintainable solutions in collaboration with engineering stakeholders.

Own the company-wide information security strategy, risk posture, governance, compliance, certifications, cloud security, AI governance, incident response, and client assurance programs. Serve as acting CISO, advise executive leadership, lead audits and major incident communications, build the security team, manage the budget, and promote secure-by-design practices and security culture across global operations.

Own end-to-end global talent acquisition across technical, AI, cybersecurity, business, sales, and product roles. Partner with hiring managers, source candidates, manage interviews and offers, maintain recruitment data, and track hiring metrics. Support onboarding, employee records, performance processes, engagement events, internal communications, and broader People Operations while providing a consistent global employee experience.

The VP of Data Science will define and execute the company’s AI strategy, transform cybersecurity products into AI-first platforms, and lead development of production-grade machine learning capabilities. Responsibilities include building data science teams, establishing MLOps and AI architecture standards, deploying autonomous and LLM-driven workflows, governing responsible AI use, partnering across Product and Engineering, and representing the company through client engagements, research, publications, and industry thought leadership.

Leads client data engineering projects from requirements through production. Designs scalable Spark pipelines, diagnoses distributed job performance issues, tunes workloads to SLAs, and establishes storage and data layout strategies. Builds reusable Python frameworks, reviews code, mentors engineers, and supports containerization, object storage, CI/CD, observability, and operational documentation. The role requires deep PySpark and Spark internals expertise, large-scale time-series processing, Delta Lake or Iceberg, self-managed clusters, and hands-on coding.

Own the strategy, roadmap, growth, and lifecycle delivery of an Exposure Management application. Translate cybersecurity, vulnerability, identity, attack-path, and control data into actionable product capabilities using AI and knowledge graphs. Partner with clients, Sales, Marketing, Engineering, and Client Solutions to prioritize features, support go-to-market activities, and drive cross-sell. Lead and develop Product Owners while advancing agile, client-focused product practices.

The Data Architect will develop and govern data models within the Security Data Fabric, ensuring data integrity and flexibility while collaborating with technical teams and utilizing AI tools to enhance decision-making and model design.

Lead design, architecture, and delivery of production-grade generative AI systems (RAG, agents, vector search, LLM-backed APIs). Drive end-to-end initiatives, establish LLMOps and AI safety, deploy cloud-native AI services, mentor engineers, and produce technical documentation and runbooks for scalable, secure, production-ready AI solutions.

Lead Strategic Services to design and deliver high-value AI and data solutions for enterprise clients. Serve as senior client advisor, drive revenue growth, oversee solution design, implementation, adoption, and feedback to Product, and build scalable delivery teams and operating models.