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Staff Big Data Engineer
Job Description
We are seeking a Staff Big Data Engineer to define, design, and drive the evolution of the data platform and pipeline architecture powering Qualys' Enterprise TruRisk Platform. This role will be responsible for building and scaling distributed data systems that process billions of events and transactions daily while providing technical leadership across multiple engineering teams.
Responsibilities
Define and drive the technical vision, architecture, and long-term strategy for the data platform and data pipeline ecosystem.
Design, build, and maintain highly scalable, high-availability data processing systems using Apache Spark, Apache Kafka, and distributed data technologies.
Lead architecture and design decisions across multiple engineering teams, ensuring solutions meet requirements for scalability, reliability, performance, security, and cost efficiency.
Design and implement event-driven, streaming, and large-scale data processing architectures to support real-time and batch workloads.
Troubleshoot, optimize, and resolve complex performance bottlenecks across data pipelines, distributed systems, and platform infrastructure.
Partner with Product Management, Professional Services, and Sales Engineering teams to evaluate technical solutions, architecture trade-offs, and platform capabilities.
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Establish engineering standards, architectural guidelines, documentation practices, and operational best practices for the data platform.
Research, evaluate, and recommend emerging technologies that improve platform scalability, performance, and operational efficiency.
Mentor and guide engineers on distributed systems design, performance optimization, and big data technologies.
Support architecture reviews, technical governance initiatives, and engineering excellence programs.
Required Qualifications
Minimum 12 years of experience in software engineering, data engineering, distributed systems engineering, or big data platform development.
Minimum 6 years of hands-on experience designing, developing, and optimizing Apache Spark-based data processing solutions.
Minimum 6 years of experience building and operating large-scale data pipelines processing billions of events or transactions per day.
Minimum 4 years of experience administering and operating Apache Kafka in production environments.
Minimum 4 years of experience designing and implementing event-driven or streaming data architectures.
Strong expertise in distributed data processing systems, data lake architectures, and large-scale platform design.
Experience delivering highly available, scalable, and fault-tolerant distributed systems in production environments.
Demonstrated experience leading architecture initiatives and influencing technical direction across multiple engineering teams.
Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related technical discipline.
Preferred Qualifications
Experience working with high-volume, multi-tenant enterprise platform environments.
Proficiency in Scala, Java, or Python for data processing applications.
Proven experience mentoring junior or mid-level engineering team members.
