QA Data Analyst & Telemetry Specialist
About the position FocusKPI is looking for a QA Data Analyst & Telemetry Specialist to join one of our clients, a high-tech SaaS company. The client team is seeking a QA Data Analyst & Telemetry Specialist to support their product quality efforts through insightful data analysis, robust reporting, and close collaboration with engineering teams. This hybrid role combines skills in data analysis, log exploration, instrumentation design, and reporting. You'll investigate product behavior through telemetry, create dashboards and written reports to surface quality risks, and support the QA team in prioritizing and validating work with data-driven confidence. You’ll be the team’s go-to person for turning raw signals into clear, actionable dashboards, ad-hoc investigations, and concise written reports that inform leadership and cross-functional teams.
Responsibilities
- Data Analysis & Log Investigation: Hunt down the right logs, trace product issues across environments, and analyze patterns in system behavior to support QA investigations and test coverage strategy.
- Instrumentation Partnership: Collaborate with engineers to define, scope, and validate telemetry requirements, ensuring the capture of high-quality signals.
- Dashboards & Visualization: Design and maintain clear, role-specific dashboards that reflect test results, coverage trends, performance metrics, and release health.
- Written Reports: Produce structured reports and summaries that combine narrative, visuals, and metrics to tell a complete story about product quality, test outcomes, and production health.
- Root Cause Support: Assist QA and dev teams with root cause investigations using log data, traces, metrics, and real-world usage patterns.
- Cross-Team Collaboration: Partner with QA, Engineering, Product, and Data teams to align on what signals matter and how they’re consumed across tools and stakeholders.
- Quality Metrics Development: Define and refine KPIs around bug trends, feature coverage, regressions, and release readiness to guide strategic QA direction.
Requirements
- Minimum of 7 years professional experience, with 3-5 years of recent cross-functional experience, ideally in large corporation environments or large teams. Maximum of 15 years of overall experience.
- Bachelor’s degree (or higher) in one of the following fields is preferred:
- Computer Science
- Data Science
- Information Systems
- Software Engineering
- Statistics or Applied Mathematics
- Electrical or Computer Engineering Equivalent practical experience is also welcome — especially in QA, data analysis, SRE, or systems engineering roles.
- Strong hands-on experience with data analysis (SQL, Python, R, or similar)
- Skilled in producing clear visualizations (e.g., Superset, Plotly, Tableau, Looker, Grafana, Power BI)
- Able to write concise reports with actionable insights — weekly summaries, defect overviews, quality scorecards, etc.
- Familiar with log analysis tools (e.g., Sumologic, Splunk, Datadog, Kibana, ElasticSearch)
- Comfortable discussing and designing instrumentation/logging with engineers
- Familiarity with QA concepts, release validation, and production monitoring
- Good Technical Skills: solid technical foundations, data quality management and data visualization, effectively troubleshoot environment setup issues and code debugging scenarios, use LLMs effectively, no use of AI assistants
- Good & strong communication skills: can adapt output to technical and non-technical audiences, effective collaboration, clear articulation of technical concepts, structured responses and straight answers, no stuttering, maintain focus
- Good problem-solving skills: structured approach, quick thinking, solid reasoning ability, exploratory thinking, and identify solutions independently
Nice-to-haves
- Background in QA, test automation, or release engineering
- Experience with OpenTelemetry, distributed tracing, or event-driven logs
- Experience in continuous delivery environments with real-time observability needs
- Prior involvement in incident reviews or quality postmortems
- Relevant certifications (e.g., Data Analytics, SQL, Tableau, or Observability/Monitoring tools) are a plus.
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