{"id":2745245,"url":"https://alion.io/job/zoominfo-principal-data-engineer","title":"Principal Data Engineer","company":{"id":3180,"name":"ZoomInfo","domain":"zoominfo.com","url":"https://alion.io/company/zoominfo","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":28,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":43,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":171500,"max":269500,"currency":"USD","period":"year","gross":null,"usd_annual":269500},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Feature Store","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Datadog","optional":true},{"name":"dbt","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Fivetran","optional":true},{"name":"MySQL","optional":true}],"status":"live","first_seen_at":"2026-10-16T18:43:15Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T18:33:22Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"ZoomInfo is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen-fast.\nWe are seeking a highly experienced, hands-on Principal Data Engineer to define and evolve the architecture, engineering standards, and operating model for our enterprise data platform.\nThis role is for a technical leader who excels at complex system design: translating business and analytical needs into scalable, reliable, secure, and maintainable data products. The Principal Data Engineer will lead critical architectural decisions across data ingestion, storage, transformation, orchestration, governance, observability, and consumption, while raising the engineering bar through mentorship, design reviews, and pragmatic best practices.\nYou will work across engineering, analytics, platform, security, and business stakeholders to create systems that make trusted data accessible, timely, and actionable at scale.\n\nWhat You’ll Do\nLead Data Architecture and System Design\nOwn the technical vision and architecture for scalable, resilient, and cost-effective data platforms and pipelines.\nDesign end-to-end data systems spanning batch and streaming ingestion, transformation, data modeling, storage, orchestration, serving layers, and consumption patterns.\nMake and document high-quality architecture decisions, including tradeoffs involving performance, reliability, scalability, latency, security, maintainability, and cost.\nDefine reusable architectural patterns for common data engineering challenges, including CDC, event-driven data flows, schema evolution, slowly changing dimensions, data quality, and multi-tenant or domain-oriented data models.\nEstablish clear boundaries and interfaces between operational systems, data platforms, analytics layers, and downstream applications.\nLead technical design reviews for high-impact initiatives and provide clear, constructive direction on implementation approaches.\nIdentify architectural risks early and drive plans to mitigate technical debt, single points of failure, data inconsistency, and operational fragility.\nSet Engineering Standards and Best Practices\nEstablish and continuously improve standards for data engineering development, testing, deployment, documentation, monitoring, incident response, and lifecycle management.\nDefine best practices for version-controlled data transformations, infrastructure as code, CI/CD, code review, automated testing, and environment promotion.\nBuild a culture of data reliability by implementing standards for data contracts, schema management, lineage, observability, freshness, completeness, accuracy, and reconciliation.\nChampion security, privacy, access control, governance, retention, and compliance requirements throughout the data lifecycle.\nDevelop practical engineering guardrails that enable teams to move quickly without sacrificing quality, trust, or operational excellence.\nDrive adoption of well-defined SLIs, SLOs, alerting, runbooks, and post-incident learning practices for critical data products.\nDrive Technical Decision-Making\nServe as a trusted technical authority for complex, cross-functional decisions involving data architecture, tooling, platform capabilities, and engineering investments.\nFacilitate technical discussions that turn ambiguous requirements into durable, well-reasoned solutions.\nEvaluate build-versus-buy decisions, platform technologies, and vendor capabilities using clear criteria and documented recommendations.\nAlign architecture decisions with organizational priorities, business outcomes, data strategy, and long-term platform sustainability.\nCreate and maintain architecture decision records, reference architectures, roadmaps, and technical standards that provide clarity across teams.\nInfluence leaders and stakeholders through concise communication of technical options, risks, tradeoffs, and recommended paths forward.\nBuild and Improve Data Products\nLead the design and development of high-value, production-grade data pipelines, datasets, and platform capabilities.\nPartner with data analysts, data scientists, software engineers, and product teams to deliver well-modeled, discoverable, and trustworthy data products.\nOptimize data systems for reliability, usability, performance, scalability, and cost efficiency.\nDrive modernization initiatives, including migration from legacy platforms, consolidation of fragmented pipelines, and reduction of manual operational processes.\nEnsure data products are designed with clear ownership, documentation, lineage, quality expectations, and support models.\nMentor and Multiply Engineering Excellence\nMentor data engineers at all levels, with a particular focus on growing senior engineers into strong technical leaders.\nProvide practical coaching on system design, data modeling, debugging, performance tuning, code quality, and production operations.\nLead architecture reviews, technical workshops, design sessions, and knowledge-sharing forums.\nEstablish reusable templates, reference implementations, and playbooks that improve consistency and accelerate delivery.\nFoster an inclusive, high-accountability engineering culture centered on thoughtful collaboration, continuous learning, and ownership.\nAct as a technical multiplier: helping teams make better decisions independently while maintaining alignment with shared architectural principles.\n\nWhat you bring\nRequired\n10+ years of experience in software engineering, data engineering, platform engineering, or a related technical discipline.\n5+ years designing and operating large-scale data platforms, distributed data systems, or enterprise-grade data products.\nDemonstrated expertise in end-to-end system design, including defining requirements, evaluating tradeoffs, designing resilient architectures, and delivering production systems.\nDeep experience with modern data architecture concepts, such as lakehouse or warehouse platforms, batch and streaming processing, data modeling, orchestration, metadata management, and data governance.\nStrong proficiency in SQL and at least one general-purpose programming language, such as Python, Java, Scala, or similar.\nExperience with distributed data processing frameworks and technologies such as Spark, Kafka, Flink, Snowflake, Databricks, BigQuery, Redshift, or equivalent platforms.\nStrong knowledge of data modeling practices across analytical, operational, and domain-oriented use cases.\nExperience establishing engineering standards for testing, CI/CD, observability, infrastructure as code, reliability, and security.\nProven ability to lead technical decisions across multiple teams without relying on direct authority.\nExceptional written and verbal communication skills, including the ability to explain complex technical concepts and tradeoffs to technical and non-technical stakeholders.\nDemonstrated experience mentoring engineers and raising technical standards across an organization.\nPreferred\nExperience with cloud-native data platforms on AWS, Azure, or GCP.\nExperience implementing data mesh, domain-oriented data product, or self-service platform patterns.\nFamiliarity with data governance, cataloging, lineage, privacy, and access-control tooling.\nExperience supporting financial, revenue, operational, customer, or enterprise analytics domains.\nExperience with ML data pipelines, feature stores, or AI/LLM data foundations.\nExperience managing major data-platform migrations, modernization programs, or large-scale architectural transformations.\nLeadership Competencies\nSystems thinker: Connects individual technical decisions to broader platform, organizational, and business outcomes.\nPragmatic architect: Balances ideal-state architecture with incremental delivery and operational realities.\nTechnical judgment: Makes clear recommendations amid ambiguity and communicates tradeoffs transparently.\nBuilder and operator: Designs for production from the beginning, including reliability, security, observability, and supportability.\nMentor and multiplier: Develops others and creates scalable mechanisms for technical excellence.\nCollaborative influencer: Builds alignment across engineering, analytics, security, product, and business teams.\nOur Technology Stack\nLanguages & Databases: Python, SQL, MySQL, NoSQL databases\nOrchestration & Transformation: Airflow, dbt\nData Platforms: Snowflake, AWS, GCP, BigQuery, Redshift\nData Integration: Fivetran, custom ETL/ELT frameworks\nObservability & Data Reliability: Datadog, monitoring, alerting, data-quality frameworks\n#LI-JH1 #LI-Remote\nActual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.\nIn addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here.\nBelow is the US base salary for this position. Additional compensation such as Bonus, Commission, Equity and other benefits may also apply.\n$171,500—$269,500 USD\nAbout us: \nZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller.\nZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information.\nZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here.\nZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status. We welcome all applicants and are committed to providing equal employment opportunities regardless of sex, race, age, color, national origin, sexual orientation, gender identity, marital status, disability status, religion, protected military or veteran status, medical condition, or any other characteristic protected by applicable law. We also consider qualified candidates with criminal histories in accordance with legal requirements.\nFor Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. 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