Full-Cycle Data Engineer

About the role

We're looking for a Full-Cycle Data Engineer to join our Data & AI team and own the flow from product data sources → modeling → dashboards → insights. You'll partner with product managers, engineers, and AI teams to turn raw product data into reliable analytics infrastructure that drives decisions across the company - from individual feature bets to CEO- and CFO-level questions. This is an end-to-end role: you'll take data products from ideation through engineering, analytics, and production deployment.

Responsibilities

• Pipelines & infrastructure
• Design, build, and deploy scalable data pipelines from product and system sources in production, using Python and orchestrators like Airflow .
• Work with distributed query engines such as BigQuery or Athena, with strong SQL throughout.
• Build and maintain semantic data models for large-scale operational systems and data lakes, manually or with tooling like dbt .
• Improve the end-to-end analytics stack, from ingestion to visualization, and collaborate with engineering on event tracking and instrumentation.
• Ensure data quality, consistency, and reliability across the stack.
• Analytics & reporting
• Build and maintain dashboards and reporting layers in tools like Looker or Metabase , optimized for performance, usability, and clarity.
• Create self-serve analytics so product and business stakeholders can answer their own questions.
• Support product experimentation: A/B testing, funnel analysis, feature adoption.
• Partnership & insight
• Translate ambiguous questions from product leads, the CEO, the CFO, and others into clear metrics, KPIs, and analytical models.
• Surface trends in usage and user behavior that influence the product roadmap and feature prioritization.
• Provide ad-hoc analysis and strategic reporting for leadership.

Requirements

• 5+ years in data engineering, data analytics, or product analytics.
• Strong SQL and hands-on experience with large-scale datasets in cloud data warehouses (BigQuery or similar).
• Production Python experience for data pipelines.
• Solid grounding in product metrics, funnels, and user behavior analysis.
• Ability to turn business questions into data models, metrics, and dashboards.
• Strong communication and cross-functional collaboration skills.
• Streaming or event-driven data systems.
• Product instrumentation and tracking design.
• AI/ML or LLM experience.
• High-scale SaaS or consumer product environments.
• About april

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Company Intel

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