Principal Data Engineer

ZoomInfo Technologies LLC•Remote-US, CA

8d•$171,500 - $269,500•Remote

About The Position

ZoomInfo is seeking a highly experienced, hands-on Principal Data Engineer to define and evolve the architecture, engineering standards, and operating model for its enterprise data platform. This technical leader translates business and analytical needs into scalable, reliable, secure, and maintainable data products, and leads architectural decisions across ingestion, storage, transformation, orchestration, governance, observability, and consumption. The role partners with engineering, analytics, platform, security, and business stakeholders to make trusted data accessible, timely, and actionable at scale. It sets technical direction and engineering practices, leads the development and modernization of production-grade data products and platforms, and mentors engineers to strengthen technical leadership across teams. ZoomInfo describes itself as a Go-To-Market Intelligence Platform serving more than 35,000 companies worldwide. The position’s US base salary range is $171,500–$269,500. Additional compensation, including bonus, commission, equity, and benefits, may also apply. Actual compensation depends on factors such as work location, qualifications, skills, experience, and training.

Requirements

At least 10 years of experience in software engineering, data engineering, platform engineering, or a related technical discipline.
At least 5 years designing and operating large-scale data platforms, distributed data systems, or enterprise-grade data products.
Demonstrated expertise in end-to-end system design, including defining requirements, evaluating tradeoffs, designing resilient architectures, and delivering production systems.
Deep experience with modern data architecture concepts, including lakehouse or warehouse platforms, batch and streaming processing, data modeling, orchestration, metadata management, and data governance.
Strong proficiency in SQL and at least one general-purpose programming language, such as Python, Java, or Scala.
Experience with distributed data processing frameworks and technologies such as Spark, Kafka, Flink, Snowflake, Databricks, BigQuery, Redshift, or equivalent platforms.
Strong knowledge of data modeling practices across analytical, operational, and domain-oriented use cases.
Experience establishing engineering standards for testing, CI/CD, observability, infrastructure as code, reliability, and security.
Ability to lead technical decisions across multiple teams without relying on direct authority.
Excellent written and verbal communication skills, including explaining complex technical concepts and tradeoffs to technical and non-technical stakeholders.
Demonstrated experience mentoring engineers and raising technical standards across an organization.

Nice To Haves

Experience with cloud-native data platforms on AWS, Azure, or GCP.
Experience implementing data mesh, domain-oriented data product, or self-service platform patterns.
Familiarity with data governance, cataloging, lineage, privacy, and access-control tooling.
Experience supporting financial, revenue, operational, customer, or enterprise analytics domains.
Experience with ML data pipelines, feature stores, or AI/LLM data foundations.
Experience managing major data-platform migrations, modernization programs, or large-scale architectural transformations.

Responsibilities

Own the technical vision and architecture for scalable, resilient, and cost-effective data platforms and pipelines.
Design end-to-end data systems covering batch and streaming ingestion, transformation, data modeling, storage, orchestration, serving layers, and consumption patterns.
Make and document architecture decisions, including tradeoffs involving performance, reliability, scalability, latency, security, maintainability, and cost.
Define reusable patterns for CDC, event-driven data flows, schema evolution, slowly changing dimensions, data quality, and multi-tenant or domain-oriented data models.
Establish boundaries and interfaces between operational systems, data platforms, analytics layers, and downstream applications.
Lead technical design reviews for high-impact initiatives and provide implementation guidance.
Identify architectural risks and drive mitigation plans for technical debt, single points of failure, data inconsistency, and operational fragility.
Establish and improve standards for data engineering development, testing, deployment, documentation, monitoring, incident response, and lifecycle management.
Define practices for version-controlled data transformations, infrastructure as code, CI/CD, code review, automated testing, and environment promotion.
Implement standards for data contracts, schema management, lineage, observability, freshness, completeness, accuracy, and reconciliation.
Champion security, privacy, access control, governance, retention, and compliance throughout the data lifecycle.
Develop engineering guardrails that support fast delivery while maintaining quality, trust, and operational excellence.
Drive adoption of SLIs, SLOs, alerting, runbooks, and post-incident learning for critical data products.
Serve as a technical authority for cross-functional decisions involving data architecture, tooling, platform capabilities, and engineering investments.
Facilitate technical discussions that turn ambiguous requirements into durable solutions.
Evaluate build-versus-buy decisions, platform technologies, and vendor capabilities, documenting criteria and recommendations.
Align architecture decisions with organizational priorities, business outcomes, data strategy, and long-term platform sustainability.
Create and maintain architecture decision records, reference architectures, roadmaps, and technical standards.
Communicate technical options, risks, tradeoffs, and recommendations to leaders and stakeholders.
Lead design and development of production-grade data pipelines, datasets, and platform capabilities.
Partner with analysts, data scientists, software engineers, and product teams to deliver well-modeled, discoverable, trustworthy data products.
Optimize data systems for reliability, usability, performance, scalability, and cost efficiency.
Lead modernization initiatives, including legacy-platform migrations, pipeline consolidation, and reduction of manual operations.
Ensure data products have clear ownership, documentation, lineage, quality expectations, and support models.
Mentor data engineers, particularly senior engineers developing into technical leaders.
Coach engineers on system design, data modeling, debugging, performance tuning, code quality, and production operations.
Lead architecture reviews, technical workshops, design sessions, and knowledge-sharing forums.
Create reusable templates, reference implementations, and playbooks to improve consistency and accelerate delivery.
Foster an inclusive, high-accountability engineering culture centered on collaboration, learning, and ownership.
Help teams make sound decisions independently while maintaining alignment with shared architectural principles.