Senior SWE, Data Centralization & Storage
AttentiveUnited States$178k – $206kPosted 23 March 2026
Tech Stack
Job Description
Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.
Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.
With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in Deloitte’s Fast 500 (four years running!), LinkedIn’s Top Startups , Forbes’ Cloud 100 (five years running!), Inc.’s Best Workplaces , and the Human Rights Campaign Foundation's Corporate Equality Index !
About the Role
Our engineering department consists of 200+ people across multiple teams, such as application development, infrastructure, data platform, machine learning, and security. We believe our company will win in the long run through product innovation. To get there, we obsess over iteratively delivering customer value through rapid prototyping and data-driven decision-making.
We’re looking for a self-motivated, highly driven Software Engineer with a strong understanding of low-level distributed systems concepts - not just tool users, but systems thinkers. We want engineers who are comfortable reasoning about how infrastructure behaves under load, how services communicate over the network, and how data flows through complex architectures. Our team is focused on building the platform to support data driven products and strategic outcomes for our customers. We do this by providing an extensible and flexible compute platform to access highly available, mission critical data sets.
What You’ll Accomplish
Architect high-throughput solutions that power our most critical operations, ensuring scalability and efficiency.
Expand and enhance our self-service platform, collaborating with cross-functional teams to fuel our AI, ML, and analytics goals.
Tackle complex distributed data challenges, streamline system integrations, and uphold high standards of quality and governance.
Champion cutting-edge technologies, keeping our platform at the forefront of industry advancements and enabling strategic outcomes.
Unify data from diverse systems, paving the way for experimentation and innovation while empowering teams with intuitive tools and frameworks.
Your Expertise
Track record of debugging issues across layers—from application logic to infrastructure bottlenecks—and understand the tradeoffs in system design, not just the settings in a managed UI.
You don’t just know how to run jobs in tools, you understand how they operate under the hood and how connected storage impacts performance like,
How simple storage API semantics (e.g. consistency, eventual visibility, multi-part uploads) affect job execution
The impact of network I/O and data locality on performance and cost
The way resource scheduling and JVM tuning influence distributed job behavior
Proven experience as a Software Engineer with a focus on high throughput scalable systems.
In-depth knowledge of high throughput processing technologies such as Spark, Flink, and/or Kafka.
Proficiency in Java and strong understanding of object-oriented design, data structures, algorithms, and optimization.
You have development experience working with data warehouse tools like Snowflake, Clickhouse, Trino, etc
You’ve experience with open source data storage formats such as Apache Iceberg, Parquet, Arrow, or Hudi ... (truncated, view full listing at source)
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