Analytics Engineer designing Power BI semantic models and scalable data architecture. Building real - time analytics infrastructure for Krux’s mining - industry SaaS products.
About the role
- Software Developer building Spark- and AWS-based analytics data platforms for Genesys Cloud. Developing automated tests, data quality controls, and AI-driven engineering workflows.
Responsibilities
- Design, build, and operate scalable Spark batch pipelines, Airflow DAGs, services, and platform tooling on AWS for reliable Genesys Cloud analytics data
- Own test strategy and test architecture for new platform capabilities
- Define unit, integration, API, data pipeline, and end-to-end validation for confident releases
- Develop automated Java and Python test frameworks and suites integrated into Jenkins CI/CD and AWS Step Functions
- Define and generate representative sampled, synthetic, and AI-generated test datasets
- Implement data quality controls and release gates for correctness, completeness, freshness, and reconciliation
- Review designs and code for testability, risk, scalability, and performance and establish production-readiness evidence
- Build MCP tools, agent skills, or agentic workflows using technologies such as Strands or LangGraph
- Participate in on-call support and root cause analysis
- Create automated regression tests and data checks to reduce recurring incidents
- Partner with developers, product teams, and platform consumers to define acceptance criteria and data quality expectations
- Share engineering knowledge across a distributed team
Requirements
- 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 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 IAM, and Amazon CloudWatch
- Practical experience using AI-assisted development tools such as Kiro, Claude, GitHub Copilot, or Cursor, with sound judgment in reviewing and validating AI-generated output
- Experience with SQL and 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 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 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
- 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
- Flexible ways of working
- Mentorship, learning programs, leadership development and education support
- Paid volunteer time
- August Free Fridays
- Well-being resources
- Regionally tailored programs for employees and their families
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