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# Software Engineer, Product Platform

- **Company:** Afterpay
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $217,800.0 - $326,800.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Systems Engineering, Cloud Computing, Databases, Data Infrastructure, Data Stores, Relational Databases, Database Theory, Software Debugging, Programming Tools, Distributed Data Store, Distributed Systems, Amazon DynamoDB, MySQL, Redis, Software Tools, Cloud Services, Ruby, Software Engineering, Datadog, Cloud Platform System, Amazon ElastiCache, Indexer, Backend, AWS Aurora, Kubernetes, Codebase, OpenSearch, Golang
- **Published:** October 2, 2026
- **Apply:** https://www.dice.com/job-detail/7cc5a28f-6266-4fb1-a3cd-6b0275e13555

## About the Role

We are looking for a Senior Software Engineer with experience in databases and a generalist mindset. You will build new database platform capabilities, improve the systems engineers depend on every day, and work across backend services, distributed systems, cloud infrastructure, and developer tooling. This is a software engineering role for someone who enjoys going deep on database problems without being limited to one technical domain. You will ask good questions, make sound technical bets, and use AI as a native part of how you explore, build, test, and learn, * A minimum of 5 years of related experience with a Bachelor's degree; or 3 years and a Master's degree; or equivalent work experience.

 * Experience building and operating backend, infrastructure, or distributed systems, with meaningful experience working with relational databases in production.
 * Strong software engineering fundamentals and the ability to work across unfamiliar systems, codebases, and technical domains.
 * A practical understanding of database concepts such as transactions, indexing, replication, schema evolution, and query performance.
 * Experience in one or more areas such as database infrastructure, distributed storage, online migrations, cloud services, Kubernetes, observability, or developer tooling. You do not need prior experience with every technology we use.
 * A track record of turning ambiguous technical problems into clear plans and working solutions.
 * Comfort debugging production systems, explaining technical tradeoffs, and partnering with engineers and technical leaders across teams.
 * Interest in using AI as part of your engineering workflow and helping others apply it thoughtfully.
 * Curiosity, judgment, and a willingness to ask questions early, go deep when needed, and take ownership beyond a single component.

Technologies We Use and Teach

 * MySQL, AWS Aurora, Vitess, DynamoDB, Redis and ElastiCache, OpenSearch, AWS infrastructure, Kubernetes, backend services and APIs, observability tooling, Go, Java, Ruby, and AI-assisted engineering tools.

## Description

Product Platform Engineering builds and unifies the foundational systems Block engineers use to create, ship, and operate products. Within Product Platform Engineering, the Data Stores team builds the database and storage platforms that help engineering teams manage data safely, reliably, and efficiently., * Design, build, and operate database and storage platform software that helps Block engineers build reliable products.

 * Build services, APIs, and developer tools that automate database provisioning, schema changes, data migrations, upgrades, and other lifecycle operations.
 * Solve database scaling and performance problems while protecting data correctness, durability, and availability.
 * Work across database behavior, application code, distributed systems, networking, and cloud infrastructure to understand problems and deliver pragmatic solutions.
 * Partner with engineering teams on schema design, query patterns, and platform adoption, turning recurring challenges into reusable capabilities.
 * Help unify and modernize existing data store platforms, reducing operational toil and making safe practices the default.
 * Lead technical projects from problem framing through implementation, testing, rollout, and operational ownership, including on-call participation and incident response.
 * Make thoughtful tradeoffs between performance, reliability, cost, maintainability, and developer experience.
 * Use AI tools to accelerate development and system understanding, while applying careful review, testing, and engineering judgment.
 * Contribute to a culture that values curiosity, direct communication, early ownership, and learning through shipping., We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.