Software Developer – Analytics Data Platform (Data Reliability)

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.

• →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

• 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

• 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

Employee Referral

If a Genesys employee referred you, please apply using the link they shared so we can connect your application to their referral.

This is an active opening at Genesys. We use Artificial Intelligence to support the hiring process, but every application is reviewed by our Talent Acquisition team, looking beyond keywords to focus on your experience and potential.

About Genesys:

Genesys® empowers more than 8,000 organizations worldwide to create the best customer and employee experiences. Genesys Cloud™ is the Agentic Orchestration Platform that securely connects people, systems, data and AI across the enterprise with built-in governance and control. As a result, organizations can drive customer loyalty, growth and retention while increasing operational efficiency and teamwork across human and AI workforces. To learn more, visit www.genesys.ai. 

Reasonable Accommodations:

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Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.

Please note that recruiters will never ask for sensitive personal or financial information during the application phase.