Staff Software Engineer (Real Time/ Streaming Data Engineer)

About this role

About Bazaarvoice   At Bazaarvoice, we create smart shopping experiences. Through our expansive global network, product-passionate community & enterprise technology, we connect thousands of brands and retailers with billions of consumers. Our solutions enable brands to connect with consumers and collect valuable user-generated content, at an unprecedented scale. This content achieves global reach by leveraging our extensive and ever-expanding retail, social & search syndication network. And we make it easy for brands & retailers to gain valuable business insights from real-time consumer feedback with intuitive tools and dashboards. The result is smarter shopping: loyal customers, increased sales, and improved products.   The problem we are trying to solve : Brands and retailers struggle to make real connections with consumers. It's a challenge to deliver trustworthy and inspiring content in the moments that matter most during the discovery and purchase cycle. The result? Time and money spent on content that doesn't attract new consumers, convert them, or earn their long-term loyalty.   Our brand promise : closing the gap between brands and consumers.   Founded in 2005, Bazaarvoice is headquartered in Austin, Texas with offices in North America, Europe, Asia and Australia.   It’s official: Bazaarvoice is a Great Place to Work in the US , Australia, India, Lithuania, France, Germany and the UK! About the Team We are building our next-generation Data Enrichment Platform and we need a heavy hitter to help us scale. We aren't just moving data; we are building the real-time engine that powers our business. As a core member of our engineering team, you will architect high-throughput pipelines, solve complex latency challenges, and define the standards for a robust "Lakehouse" architecture. If you obsess over JVM internals, distributed consistency, and Exactly-Once semantics, this is the role for you.   How You'll Make an Impact: Architect at Scale: Lead the design of distributed streaming architectures using Kafka Streams, Spark, and Flink. You will build the roadmap for our transition to a robust Lakehouse architecture. Performance Tuning: Dive deep into the system to identify bottlenecks. You will own the stability of pipelines processing massive events per second, solving for data integrity and low latency. Quality by Design: Set the engineering standard. You will drive a culture of reliability through robust monitoring (Datadog/Prometheus), observability, and automated testing frameworks. Modernization: proactively identify opportunities to refactor and modernize the platform for maximum efficiency and cost-effectiveness.   The Must Have Skills: Veteran Engineer: 8+ years of software engineering experience, with at least 5 years of deep production expertise in Scala or Java (concurrency, memory management, GC tuning). Deep JVM knowledge required. Streaming Expert: 5+ years hands-on with the Kafka ecosystem. You understand offsets, partitions, rebalancing, and state stores intimately. Apache Kafka, Kafka Streams, Kafka Connect, Flink, Spark. Data Architect: You have a proven track record of designing systems that remain resilient under heavy load. You understand the trade-offs between consistency models and storage formats. AWS Native: Mastery of the AWS ecosystem (handling TBs of data) is second nature to you. AWS (MSK, EMR, Athena, Lambda), Terraform, Kubernetes.   Why You’ll Love Working with Us? Work with cutting-edge technology in a collaborative, global team Competitive salary + good benefits (insurance, annual leave, bonuses, referral rewards, and more). We’re Great Place to Work Certified (4 years in a row!). Hybrid work model (3 days in office – Prestige Tech Pacific).   Interview Process: Tech Round 1: Coding interview with our team. Tech Round 2: System design discussion. Hiring Manager Meeting: Team, values & culture check. Leadership: AI & Solution design discussion Final Round: Chat with HR/Senior Leadership.   If this sounds like you, let’s talk