T
Toast
Staff Software Engineer (Data Platform)
Skills
KotlinDynamoDBJavaPostgreSQLKafkaAIAWS
20 hours ago
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Important information
Contract type:
Permanent
Location:
Ireland
Work mode:
Hybrid
Published on:
22 October 2026
What they need
- We are hiring a Staff Software Engineer to join our data platform (AAP) team in Dublin
- The AAP team owns the foundational data infrastructure that powers Toast’s analytics products and AI/ML capabilities: real-time streaming pipelines, a cloud-native data lakehouse, feature serving for ML-driven product experiences, and the infrastructure that enables product teams across Toast to build intelligent, data-driven experiences for our 170,000+ restaurant customers
- We are in the middle of a significant platform evolution: migrating to Apache Iceberg, standing up a new Feature Store, conducting Databricks evaluations, and scaling our streaming infrastructure to meet the demands of a rapidly growing business
- As a Staff Software Engineer, you’ll be the technical lead for major parts of this evolution
- You’ll design and lead the delivery of platform capabilities that several teams depend on, doing deep hands-on work and making design calls that account for streaming, storage, serving, and the teams consuming them
- You’ll be the go-to expert in your area of the platform, working with product and engineering partners inside and outside our line of business
- You’ll make the engineers around you better through mentorship, design reviews, and the example you set
- Success looks like other engineering teams moving faster because of the platform you help build
- Lead the delivery of significant data platform capabilities, from early design through production rollout, across the AAP team and the teams that depend on it
- Work hands-on with Kotlin/Java, Spark, Iceberg, and our streaming infrastructure, diagnosing performance, reasoning through trade-offs, and writing the code that sets the pattern for others
- Anticipate how requirements will change and design systems that can absorb that change, exploring the problem fully before settling on a solution
- Evolve the platform’s API surfaces so that engineers across Toast can self-service - adoption and ease-of-use are part of the job
- Lead efforts that measurably improve platform quality, reliability, and observability for the teams and customers who depend on it
- Mentor other engineers and raise the bar on engineering practices across the organisation
- Use AI tooling as a genuine force multiplier in your day-to-day work and help the team do the same
- Our tech stack:
- Our data platform runs on AWS and spans Apache Iceberg for table format and lakehouse architecture, Apache Kafka and Pulsar for real-time streaming, Apache Spark for data processing, and DynamoDB and Postgres for operational data stores
- We are migrating from legacy ETL patterns toward a modern, cloud-native data lakehouse and building out Feature Store capabilities to power ML-driven product experiences
- Our backend services are primarily Kotlin and Java
Benefits
- Peer and company recognition programs
- Unlimited Vacation
- Sabbatical opportunity after five years
- Professional Development Reimbursement Program
- Commitment to Employee Wellness through resources such as a quarterly Wellness Stipend
- Various peer and company recognition programs
- 401(k) and matching
- Medical, Dental, & Vision Coverage
- Subsidized backup childcare
- Mental Health Benefits- Experience building internal platforms and developer-facing abstractions - you think about your platform’s users as carefully as product engineers think about end users
- 5+ years in software engineering with a strong focus on data platforms or distributed systems, including technical leadership of projects involving more than one team
- Experience with streaming infrastructure (Apache Kafka, Apache Pulsar, or similar) and cloud-native data platforms on AWS
- Databricks experience is a bonus but we care more that you understand what’s running underneath it
- Proficiency in Java, Kotlin or other object-oriented language
- Strong hands-on expertise in Apache Spark and Apache Iceberg (or similar)
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