Software Developer – Analytics Data Platform (Data Reliability)

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, and platform consumers to define acceptance criteria, align on data quality expectations, and share engineering knowledge across a distributed team

Required Qualifications:

• •Master's degree in Computer Science or a related field, or a bachelor's degree in Computer Science or a related field plus 2+ years of experience in software development, quality engineering, or test automation
• •Strong proficiency in Java and hands-on development experience with Python
• •Experience developing automated tests in code using frameworks such as JUnit, TestNG, pytest, or REST Assured, including integration or end-to-end testing for APIs, services, or data pipelines
• •Experience with CI/CD practices using Jenkins or an equivalent platform, along with Git-based development workflows
• •Working knowledge of AWS services such as Amazon S3, Amazon EMR, Amazon EC2, AWS Lambda, AWS Identity and Access Management (IAM), and Amazon CloudWatch
• •Practical experience using AI-assisted development tools such as Kiro, Claude, GitHub Copilot, or Cursor, combined with sound judgment in reviewing and validating AI-generated output
• •Experience with SQL and demonstrated ability to investigate data, application behaviour, and system signals to identify root causes
• •Ability and willingness to participate in an on-call rotation and troubleshoot production incidents
• •Clear written and verbal communication skills, with the ability to collaborate effectively and work independently within a distributed team

Preferred Qualifications:

• •Hands-on experience with Apache Spark for large-scale batch processing, preferably using Amazon EMR
• •Experience writing, testing, or operating Apache Airflow DAGs
• •Familiarity with big data technologies such as AWS Glue, Amazon Athena, or Apache Iceberg
• •Experience with data quality engineering, including pipeline output validation, reconciliation, data contracts, and schema-evolution testing
• •Experience generating test data at scale using sampled, synthetic, or AI-generated datasets while respecting applicable privacy requirements
• •Experience with performance or scalability testing for batch processing or distributed systems
• •Knowledge of pipeline observability practices, including metrics, logs, tracing with OpenTelemetry, and alerting
• •Experience contributing to shared test frameworks or platform tooling used across multiple engineering teams
• •Experience with multi-tenant SaaS platforms or privacy-related data lifecycle requirements, including GDPR data deletion
• •Familiarity with building MCP tools, agent skills, or agentic workflows using frameworks such as Strands or LangGraph

• Benefits That Support You:
• We invest in your well-being, because when you’re at your best, so are the experiences you create.

• •Comprehensive extended group health coverage
• •Generous paid time off, including vacation and personal leave
• •Retirement savings program with employer RRSP matching up to a prescribed maximum amount
• •Family-friendly benefits, including parental leave top-up and adoption assistance
• •Growth and development opportunities through access to learning resources and internal mobility programs

Exact details of these programs, including the conditions for eligibility, the amount of the benefit (including caps on benefits and customary pricing details), the length of the paid time off, etc. will be provided upon hire and is subject to Genesys policies.

Compensation:

This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location. This role might also be eligible for a commission or performance-based bonus opportunities.

• $102,900.00 - 135,000.00
• Working at Genesys

• •AI at enterprise scale – Build, support and operate AI-powered technology used by more than 8,000 organizations worldwide. 150+ new AI features were released in the last fiscal year.
• •A flex