Senior Data Engineer
Zoom · India · 8+ yrs experience · Posted 2026-07-18
Tech stack: Go, Python, SQL
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About the role
You will design and scale the data systems that connect product usage to revenue outcomes. You will build real-time and batch pipelines powering predictive models and business activation. You will shape foundational architecture that drives company-wide decision-making. Our team builds the data platform powering revenue intelligence across products. We partner with engineering, data science, and go-to-market teams. We turn raw telemetry into trusted, actionable data at scale.
Responsibilities:
- Designing and building end-to-end data architecture that unifies product telemetry, customer lifecycle, and revenue metrics into a trusted single source of truth.
- Developing and maintaining scalable pipelines — both Distributedreal-time and batch — that integrate product usage, CRM, and billing systems to enable closed-loop revenue attribution.
- Leading implementation of a standardized telemetry framework across platforms, defining event contracts and solving identity resolution challenges across devices and products.
- Building data foundations for machine learning by partnering with data science to deliver feature pipelines, training datasets, and low-latency scoring infrastructure that accelerate experimentation.
- Establishing reliability and governance standards including data quality frameworks, monitoring, incident response, privacy compliance, and self-service data modeling practices.
Qualifications:
- 8+ years of experience in Data Engineering / Distributed Systems.
- Demonstrate extensive experience designing and operating large-scale data platforms in production environments with measurable business impact.
- Build production-grade data pipelines using Python and advanced SQL, with a focus on performance optimization and reliability.
- Operate modern data warehouses (such as Snowflake or Databricks) and orchestration tools (such as Airflow or Dagster) to manage complex data workflows.
- Integrate diverse data sources — including event telemetry, CRM, and billing systems — into cohesive, well-modeled data products.
- Apply data modeling best practices (using dbt or equivalent) to create testable, documented, and backward-compatible models that enable self-service analytics.
- Bring experience in SaaS or product-led growth environments, including familiarity with product analytics platforms or streaming systems.
- Support machine learning workflows by building feature stores, training pipelines, or real-time scoring infrastructure, or demonstrate equivalent practical experience.
- Leverage AI-assisted development tools to accelerate engineering workflows, contribute to reusable automation, and continuously adopt emerging tooling to enhance team productivity.
Qualifications
- 8+ years of experience in Data Engineering / Distributed Systems.
- Demonstrate extensive experience designing and operating large-scale data platforms in production environments with measurable business impact.
- Build production-grade data pipelines using Python and advanced SQL, with a focus on performance optimization and reliability.
- Operate modern data warehouses (such as Snowflake or Databricks) and orchestration tools (such as Airflow or Dagster) to manage complex data workflows.
- Integrate diverse data sources — including event telemetry, CRM, and billing systems — into cohesive, well-modeled data products.
- Apply data modeling best practices (using dbt or equivalent) to create testable, documented, and backward-compatible models that enable self-service analytics.
- Bring experience in SaaS or product-led growth environments, including familiarity with product analytics platforms or streaming systems.
- Support machine learning workflows by building feature stores, training pipelines, or real-time scoring infrastructure, or demonstrate equivalent practical experience.
- Leverage AI-assisted development tools to accelerate engineering workflows, contribute to reusable automation, and continuously adopt emerging tooling to enhance team productivity.
Responsibilities
- Designing and building end-to-end data architecture that unifies product telemetry, customer lifecycle, and revenue metrics into a trusted single source of truth.
- Developing and maintaining scalable pipelines — both Distributedreal-time and batch — that integrate product usage, CRM, and billing systems to enable closed-loop revenue attribution.
- Leading implementation of a standardized telemetry framework across platforms, defining event contracts and solving identity resolution challenges across devices and products.
- Building data foundations for machine learning by partnering with data science to deliver feature pipelines, training datasets, and low-latency scoring infrastructure that accelerate experimentation.
- Establishing reliability and governance standards including data quality frameworks, monitoring, incident response, privacy compliance, and self-service data modeling practices.