Back-end Java and Python developer responsible for building, maintaining, and evolving APIs, microservices, and integrations in mission-critical environments.
Locations (2)
Santo Amaro, São Paulo, Brazil
Sao Paulo, São Paulo, Brazil
Responsibilities
Design, develop and maintain back-end services using Java and Python following clean architecture and best engineering practices.
Create and consume RESTful and GraphQL APIs, ensuring consistency, versioning and proper documentation (Swagger/OpenAPI).
Collaborate with front-end developers, DevOps, QA and product owners for continuous delivery of features.
Ensure performance, availability and security of services through testing, logging, monitoring and code tuning.
Participate in code reviews, estimations and technical solution definition.
Build the foundation other engineers stand on, including service frameworks, shared libraries, observability, and resilience for Nominal’s mission-critical software.
Responsibilities
Build service frameworks and shared libraries for Nominal services
Lead production infrastructure ownership for two SaaS platforms, ensuring reliability, scalability, security, and cost efficiency across US and EU regions.
Compensation
Compensation: USD-based, to be determined based on experience and technical expertise.
Responsibilities
Define, implement, and maintain Service Level Objectives (SLOs) for critical customer-facing workflows
Own and evolve production infrastructure across GCP and/or AWS, including Kubernetes, networking, autoscaling, and upgrades
Manage ClickHouse and relational databases, including replication, backups, recovery testing, and capacity planning
Maintain and improve infrastructure automation using Terraform or similar tools
Own observability systems using Grafana, Sentry, and related tools
Requirements
Nice to have: AI coding agents such as Claude Code
As a Staff Software Engineer, you will act as the technical lead for a key product or platform domain, owning its technical direction, architecture, delivery, and long-term health for the Japan market.
Responsibilities
Own the technical direction and architecture of a product or platform domain.
Act as the technical lead for the engineering team, providing direction, design guidance, and mentorship.
Work closely with Architects and engineering leadership to align domain solutions with the broader platform strategy.
Lead complex initiatives spanning multiple services, systems, and teams.
Drive decisions around service design, APIs, messaging, caching, data ownership, resilience, and scalability.
As a Staff Solutions Engineer at Kong, you’ll be a trusted technical advisor helping the world’s most innovative enterprises architect secure, scalable API ecosystems while navigating the transition into the agentic AI era.
Responsibilities
Support field sales in helping customers understand the value of the Kong API and AI connectivity platform.
Conduct discovery conversations to understand customer requirements and map them to Kong solutions, from initial product reviews through to production.
Support POCs throughout the entire evaluation process, owning technical validation and stakeholder alignment.
Ensure customer satisfaction across your assigned set of accounts.
Collaborate with support, product, and engineering teams to advocate for customer needs.
Design the end-to-end data platform for APEX, ingesting contact centre telephony and quality data, and delivering it through a Bronze/Silver/Gold architecture on Databricks into a Kimball-style reporting layer.
Locations (2)
Mississauga, Ontario, Canada
Toronto, Ontario, Canada
Compensation
Competitive Pay and Benefits: enjoy a comprehensive compensation and benefits package, including health insurance, and a relocation program.
Responsibilities
Design the Bronze/Silver/Gold data flow for the new Genesys A3S Snowflake and Verint sources, with clear standards for ingestion, transformation, validation, and consumption.
Define ingestion cadence and watermarking strategy per source, based on each source's true data freshness.
Design the Gold-layer Kimball star schema (facts, dimensions, grain, SCD Type I/II) to support sales, telephony, agent activity, and quality reporting.
Choose between pure lakehouse and hybrid lakehouse-to-data-warehouse patterns based on source characteristics, volumes and performance needs.
Build and optimize Databricks and Delta Lake pipelines, and set cost and performance practices for cluster sizing, job versus all-purpose compute, and Photon.
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