Be the one building AI-powered experiences where they matter most
At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships.
Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day.
Role Overview: (Remote Canada)
The Analytics Data Platform team builds the data foundation that powers analytics and insights across Genesys Cloud, processing petabyte-scale customer experience data where correctness, completeness, and timely delivery directly influence the experiences our customers can provide. As a Software Developer, you will own meaningful parts of this platform across data engineering, quality engineering, and production reliability, using Spark, Apache Airflow, Apache Iceberg, Java, Python, and AWS to build scalable batch pipelines, services, and platform capabilities.
Quality engineering is a core part of the role. You will shape test strategy and architecture, determine how new capabilities are validated, and build the automated tests, realistic test data, data quality controls, and CI/CD gates that give the team confidence to release. You will also follow what you build into production, participate in on-call support, and turn operational lessons into stronger automated safeguards.
You will help advance an AI-driven software development lifecycle by moving beyond AI-assisted coding into practical engineering workflows, including MCP tools, agent skills, and agentic solutions that improve debugging, incident triage, and test generation. Your work will influence how the team adopts AI-driven development and testing while maintaining the engineering judgment needed to validate AI-generated output.
At Genesys, we use AI-powered experience orchestration to help organizations create more personalized customer and employee experiences. This role gives you exposure to platform-level engineering at significant scale, opportunities to expand your expertise across distributed data systems and AI-enabled engineering, and the ability to influence practices used by teams that depend on the Analytics Data Platform.
Key Responsibilities:
Design, build, and operate scalable Spark batch pipelines, Airflow DAGs, services, and platform tooling on AWS that deliver reliable analytics data across Genesys Cloud
Own test strategy and test architecture for new platform capabilities, defining the appropriate unit, integration, API, data pipeline, and end-to-end validation needed for confident releases
Develop automated test frameworks and suites using Java and Python, integrating validation into Jenkins CI/CD and AWS-native orchestration such as Step Functions for stateful, multi-step testing
Define and generate representative test datasets using sampled and synthetic data, including AI-generated data where appropriate, to validate edge cases, multi-tenant scenarios, privacy requirements, and schema evolution
Implement data quality controls and release gates that identify correctness, completeness, freshness, and reconciliation issues before they affect downstream teams or customers
Drive release readiness by reviewing designs and code for testability, risk, scalability, and performance, and by establishing clear evidence that changes are ready for production
Advance AI-driven engineering practices by building MCP tools, agent skills, or agentic workflows using technologies such as Strands or LangGraph to improve debugging, triage, and automated test generation
Strengthen production reliability through on-call participation, root cause analysis, and automated regression tests or data checks that reduce the likelihood of recurring incidents
Partner with developers, product teams,…