Who Are We

Gen is not just a company; we’re a global force. Fiercely committed to ensuring that everyone, everywhere, can live their lives digitally safe. Our family of brands – MoneyLion, Norton, Avast, LifeLock, AVG, Reputation Defender and CCleaner – unite the brightest minds, the sharpest tech and the most diverse thinking to protect over 500 million people. And we’ve built an inclusive workplace, where your well-being is a priority because true success comes from a place of balance and authenticity. When you're thriving, you’re unstoppable. So, bring us your bold ideas and passion that refuses to quit. The digital world isn’t some distant reality – it's the world we live in, and we’re ready for it. If you’re ready to push boundaries and be part of something bigger, join #TeamGen.

How We Work

We are a thriving, globally distributed team focused on building innovative mobile app solutions in security, utilities, and finance. Leveraging modern technologies, including AI-powered tools, we experiment, evaluate, and drive impactful change, creating secure, high-performance products across the globe.

Mission and Goals

We are seeking an exceptional Staff Data Engineer to join our team as a senior technical leader responsible for architecting and implementing enterprise-scale data solutions that directly impact our cybersecurity platform's strategic objectives. This role combines deep technical expertise with significant organizational influence, requiring someone who can design complex data architectures, mentor engineering teams, and drive the technical vision for our multi-petabyte data infrastructure serving millions of users.

Objectives

• Expertise with Apache Kafka, real-time data processing, and event-driven architectures

• Experience with Apache Iceberg, data lakehouse architectures, and modern table formats

• Demonstrated expertise in cloud cost optimization and data warehouse performance tuning

• Design and architect enterprise-scale data solutions that directly impact Gen's data strategy execution. This might be something like designing and implementing an incremental identity graph, integrating a marketing attribution vendor or automating LTV calculation.

• Lead evaluation and implementation of cutting-edge technologies including modern data stack and advanced analytics

• Collaborate with executives and senior leadership to align data architecture with business strategy

• Mentor IC9 and IC10 engineers, focusing on architectural decision-making and advanced technical skills

• Drive complex, multi-quarter technical initiatives requiring coordination across multiple engineering teams

Competencies

• Bachelor's degree in computer science, Data Engineering, or related technical field (master's degree preferred)

• Minimum 10-15 years of hands-on data engineering experience in large-scale, high-volume environments

• Expert-level proficiency in SQL optimization for multi-petabyte datasets and advanced Python programming

• Extensive experience with Snowflake architecture, optimization, and cost management at enterprise scale

• Demonstrated expertise building Kimball dimensional data warehouses and star schema architectures

• Advanced experience with AWS cloud infrastructure and either DBT or SQLMesh for data transformations

• Hands-on experience with CI/CD pipelines, GitFlow, and data governance frameworks ensuring GDPR/PCI-DSS compliance

• Proven ability to architect and own multiple entire areas of the codebase or services independently

• Experience with MLOps frameworks, LLM integration, vector databases, and AI-driven analytics. We know its new, but some familiarity is a plus.

• Proven track record with subscription business models including cohort analysis, LTV calculations, and marketing attribution

• Direct industry experience in cybersecurity, ad-tech, or high-volume data processing environments (billions of events per day)

• This position requires a unique combination of deep technical expertise, strategic thinking, and leadership capabilities. The successful candidate will be a recognized expert in their domains within the broader data engineering community and capable of driving significant technical and business impact at enterprise scale.