Data Engineer / BI Developer

Job Summary:

Own the quality strategy and executable assurance for onboarding self-service data and BI solutions within the DA&A domain. This role validates functional behavior, module contracts, idempotency, parallel execution, approvals, failure diagnostics, resume/recovery, telemetry, security-adjacent controls, performance evidence, and operational readiness across defined data application patterns and multiple environments. Candidate must have relevant experience of 3 to 6 Years.

Key Responsibilities:

• Own the quality strategy and executable assurance for the onboarding self-service solution for data and BI workloads.
• Validate functional behavior, module contracts, idempotency, parallel execution, approvals, failure diagnostics, resume/recovery, telemetry, security-adjacent controls, performance evidence, and operational readiness across the defined application patterns and eight environments.

Quality planning and traceability

• Create the master test strategy, test pyramid, environment approach, entry/exit criteria, defect workflow, evidence standards, and acceptance traceability matrix.
• Map tests to both application patterns, all eight environments, one-time and per-environment modules, orchestration, approvals, dashboard, email, and handover deliverables.
• Define test data and naming practices that avoid collisions and permit safe cleanup and repeat execution.

Automation and functional validation

• Automate request-schema/default validation, module-level checks, integration tests, and end-to-end onboarding tests.
• Validate repository scaffolding, starter application health check, CI/CD asset generation, Terraform outcomes, Helm releases, GitOps registration, access, and secrets references.
• Confirm that Release Workflow request, trigger, run status, approval evidence, and final outcome are correct and auditable.
• Validate that the five NonProd environments execute in parallel and production environments run only after the required approval.

Resilience, recovery, and non-functional testing

• Execute repeatability and idempotency tests to confirm reruns do not create duplicate or conflicting resources.
• Inject controlled failures at module and orchestration steps; verify diagnostics, retained state, retry behavior, resume point, and audit history.
• Test invalid requests, missing access, unavailable dependencies, approval rejection/hold, unstable environments, timeouts, partial provisioning, and GitOps/health-check failures.
• Measure happy-path elapsed time against the current baseline and report actual results without treating the design target as an SLA.
• Validate that sensitive values do not appear in logs, generated summaries, dashboard content, or test artifacts.

Operational readiness and acceptance

• Validate dashboard calculations and views for runs, success rate, elapsed time, and failures by step; verify summary-email completeness and accuracy.
• Produce the formal test report covering repeatability, idempotency, failure/recovery, and elapsed-time results.
• Review runbooks and troubleshooting guides by executing recovery procedures; identify gaps before knowledge transfer.
• Provide gate-ready quality evidence, support demonstrations and production launch, and verify warranty defect fixes.

Other Capabilities:

• Apply quality engineering practices to data pipelines, BI models, and analytics platforms using iCEDQ or similar data testing tools.
• Collaborate with Data Engineers, BI Developers, platform teams, and SRE/DevOps engineers to design testable data and automation patterns.
• Ensure traceability from data requirements and SOW acceptance criteria through test scenarios, automated checks, evidence, defects, and gate sign-off.
• Contribute to continuous improvement of data quality, observability, and operational readiness across DA&A solutions.
• Communicate complex quality findings clearly to technical and non-technical stakeholders and support production launch decisions.

Qualifications and Skills:

Required experience and qualifications

• 5-8 years in quality engineering, test automation, DevOps/platform testing, SRE validation, or infrastructure automation assurance.
• Hands-on experience testing CI/CD pipelines, APIs, infrastructure as code, Kubernetes/AKS deployments, or enterprise workflow integrations.
• Strong automation skills in Python, PowerShell, Bash, or a comparable language, with API and command-line validation experience.
• Experience designing test evidence for milestone acceptance and production-readiness decisions.
• Bachelor's degree in a technical discipline or equivalent practical experience.

Mandatory technical competencies

• Test strategy for distributed automation spanning request forms, APIs, Jenkins orchestration, Terraform, CI/CD assets, Helm, GitOps, secrets, and AKS.
• Automated API/schema/configuration validation, pipeline test hooks, infrastructure assertions, Kubernetes health checks, and end-to-end verification.
• Idempotency, repeatability, concurrency/parallelism, negative testing, failure injection, retry/resume, partial-state, and recovery testing.
• Telemetry validation covering run IDs, step timings, logs, audit records, dashboard measures, failure classification, and summary-email accuracy.
• Defect isolation across workflow, orchestration, cloud provisioning, pipeline generation, deployment, access, secrets, policy, and environment layers.
• Traceability from SOW acceptance criteria to test scenarios, automated checks, evidence, defects, and gate sign-off.

Preferred / value-adding experience

• Experience with pytest, Pester, REST-assured/Postman/Newman, Terratest, Helm test, Kubernetes test tooling, or equivalent approaches.
• Experience with chaos/failure-injection techniques in controlled NonProd environments.
• ISTQB, Azure, Kubernetes, or DevOps-related certification.
• Experience validating dashboards/telemetry and operational runbooks for platform products.

Role-specific skills

• Experience in Data Automation & AI within DA&A environments.
• Hands-on experience with Quality Engineering – Data Testing using iCEDQ or comparable data testing tools.
• Ability to design and execute data validation, reconciliation, and regression suites for data pipelines and BI solutions.
• Understanding of data warehousing, ETL/ELT processes, and BI/reporting workflows from a testing and automation perspective.