# Senior Data Engineer

**Company:** [ZoomInfo](https://hotfix.jobs/companies/zoominfo)
**Location:** Remote
**Role:** Data Engineering
**Experience:** 8+ years
**Skills:** SQL, Python, Airflow, dbt, Snowflake, Kafka, GCP, AWS, Terraform, Datadog, Monte Carlo, Grafana, Pagerduty, Etl/Elt, CI/CD
**Posted:** 2026-08-07

> Designs and operates scalable enterprise data pipelines, warehouses, and lakehouse infrastructure using Python, SQL, dbt, Airflow, and Snowflake. The role requires 8+ years of data engineering experience, production ownership, cloud expertise, and strong architectural and mentoring skills.

## Job Description

## Responsibilities
- Design, develop, and maintain high-performance, product-centric data pipelines using **Airflow**, **dbt**, and **Python**.
- Architect and optimize a massive-scale data warehouse and lakehouse, primarily using **Snowflake**.
- Integrate structured and unstructured data sources, including web data and third-party APIs, into the data ecosystem.
- Define roadmap priorities for internal data consumers and data and AI capabilities.
- Advise leadership on strategy, AI readiness, and data infrastructure investments.
- Collaborate with ML engineers, data scientists, and product managers to deliver scalable data solutions.
- Define, monitor, and enforce data quality SLAs, including data accuracy and lineage.
- Participate in a shared **PagerDuty** on-call rotation, respond to incidents, perform root-cause analysis, and drive remediation and postmortems.
- Triage production issues and escalate based on severity, blast radius, and customer impact.
- Make sound decisions amid ambiguity and iterate with stakeholders.
- Mentor junior engineers and promote best practices in code quality, data architecture, incident response, and operational excellence.
- Participate in architectural decisions and long-term enterprise data infrastructure strategy, focusing on cost, performance, reliability, and observability.
- Maintain runbooks, on-call documentation, and operational playbooks.

## Requirements
- Expert-level **SQL** for performant, scalable queries and transformations on massive datasets.
- Strong **Python** programming skills, including distributed computing, data manipulation, and robust API development.
- Production experience with large-scale batch and streaming data processing.
- Hands-on experience with **dbt** for advanced data modeling and transformations.
- Deep knowledge of **Snowflake** data warehouse design, optimization, and cost modeling.
- Experience owning production systems, including on-call rotations, incident response, and postmortems.
- Understanding of data lakes, event-driven architectures, **Kafka**, ETL/ELT, and data mesh.
- Proficiency with **GCP** and/or **AWS** and infrastructure as code such as **Terraform**.
- Experience with monitoring and observability tools such as **Datadog**, **Monte Carlo**, and **Grafana**.
- Familiarity with CI/CD practices for data workflows, automated pipeline testing, and version-controlled data models.
- Excellent communication and stakeholder management skills.
- Strategic, product-oriented thinking and the ability to translate business needs into scalable data solutions.
- Leadership, mentorship, ownership, accountability, sound judgment, and strong documentation habits.
- Bachelor's or master's degree in Computer Science, Engineering, or a related field.
- 8+ years of progressive data engineering experience.
- Experience implementing or scaling data infrastructure for a data-centric product company.
- Experience participating in an on-call rotation supporting production data systems.

## Compensation and Benefits
- Compensation and benefits details are not specified in the provided posting.

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