Renesas is one of the top global semiconductor companies in the world. We strive to develop a safer, healthier, greener, and smarter world, and our goal is to make every endpoint intelligent by offering product solutions in the automotive, industrial,...
About the role
This role is responsible for the complete analytics data lifecycle — starting with analytics instrumentation embedded in product source code, through event collection and ingestion, raw data storage, transformation and aggregation pipelines, and ultimately the presentation of trusted analytics through dashboards, reports, and analytical tools.
What they're looking for
- Strong software engineering and data engineering background
- Hands-on experience building and operating production analytics or data platforms
- Advanced SQL skills
- Experience designing ETL/ELT and data transformation pipelines
- Experience with event-based product analytics
- Understanding of analytics instrumentation within web applications, backend services, and distributed systems
More about this role
We are looking for a highly technical Senior Analytics Engineer to own and evolve our end-to-end product analytics platform.
This role is responsible for the complete analytics data lifecycle — starting with analytics instrumentation embedded in product source code, through event collection and ingestion, raw data storage, transformation and aggregation pipelines, and ultimately the presentation of trusted analytics through dashboards, reports, and analytical tools.
The successful candidate will act as the owner of the analytics flow, ensuring that analytics data is accurate, reliable, scalable, well-defined, and usable across the organization.
This is not primarily a dashboard-building or reporting role. It is an engineering-focused position responsible for the architecture, implementation, operation, and continuous improvement of the analytics platform.
Key Responsibilities
- End-to-End Analytics Platform Ownership
- Product Analytics Instrumentation
- Data Collection and Raw Data Layer
- Data Pipelines, Importers, and Processing
- Analytics Data Models and Metrics
- Analytics Presentation Layer
- Data Quality and Observability
- Analytics Governance
Expected Outcomes
The...
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