At ZoomInfo we encourage creativity, value innovation, demand teamwork, expect accountability and cherish results. If you are collaborative, take initiative, and get stuff done we want to talk to you! We have high aspirations for the company and are looking for the right people to help fulfil the dream. We strive to continually improve every aspect of the company and use cutting-edge technologies and processes to delight our customers and rapidly increase revenue. 

Role Overview: ZoomInfo is seeking a Data Analyst III to join the Data Operations and Analysis team. This role partners closely with Product, Engineering, and Research teams to enhance data infrastructure, improve data quality, and deliver actionable insights that drive business impact.

Key Responsibilities:

• Develop a deep understanding of data systems, pipelines, and infrastructure.
• Analyze current data processes, identify gaps, and recommend scalable improvements.
• Drive initiatives to enhance data accuracy, coverage, and efficiency.
• Collaborate cross-functionally with Data Engineering, Product, and Research teams.
• Perform advanced data analysis and present insights in a clear, structured manner.
• Mentor junior analysts and contribute to team capability building.
• Work independently to explore data, propose solutions, and execute projects end-to-end.

Core Competencies:

• Strong analytical thinking and problem-solving ability.
• Ability to work in ambiguous environments and drive clarity.
• Effective communication with both technical and non-technical stakeholders.
• High attention to detail and strong organizational skills.
• Self-driven with the ability to manage multiple priorities.

Required Qualifications:

• 5+ years of experience in analytics, data operations, or quantitative research.
• Advanced proficiency in SQL, Python, BigQuery, and Excel.
• Strong understanding of ETL processes and data quality challenges.
• Experience working with large-scale datasets.
• Familiarity with AI/ML concepts including NLP and data modeling.

Preferred Qualifications:

• Experience building or deploying AI/ML solutions.
• Exposure to tools like Tableau or Looker Studio.
• Experience with Databricks, AWS, or Airflow.
• Background in product-driven or project-based environments.

Impact: This role plays a critical part in shaping the future of ZoomInfo’s data ecosystem by improving data reliability, scalability, and strategic value.

#LI-SG2

#LI-Hybrid

The Role

ZoomInfo is building its Forward Deployed Engineering function. You will define how it operates — how engagements run, what the deliverables are, how the playbook works, what scales and what doesn't. The support structure exists (data team, product team, infrastructure, executive sponsorship); what's missing is the person who brings it to life in front of customers and turns early wins into a repeatable model.

As an FDE, you embed directly with ZoomInfo's strategic accounts — large enterprises with complex data needs, often in financial services, insurance, technology, and other globally distributed industries. You work alongside their teams to understand their go-to-market challenges, then design and deliver bespoke intelligence applications that combine ZoomInfo's third-party data with the customer's first-party data to drive real business outcomes.

You own the engagement end-to-end: from discovery through deployment, from executive presentation through production code. You'll work closely with our data and product teams to bring the full breadth of ZoomInfo's data foundation to bear — company intelligence, contact data, buying signals, intent data, and specialized vertical datasets — assembled into purpose-built applications tailored to each customer's specific personas and workflows.

ZoomInfo has one of the deepest go-to-market data foundations in the world — 500M+ professional profiles, 100M+ company records, intent signals, vertical datasets spanning financial filings, insurance, commercial fleet, and more. You'll have access to incredible data, powerful infrastructure, and our most important customer relationships. What you build with them — and how you build it — will define the model going forward.

What Engagements Typically Look Like

A few recurring patterns show up across strategic accounts. Most engagements involve some combination of:

• Entity resolution at scale — reconciling legal entity hierarchies (D&B, tax IDs, company-house registrations) with how customers actually go to market. Multinationals with hundreds of legal entities collapsing to a single GTM record.
• Hierarchy management — enforcing one-to-one matching across regions, fixing parent-child linkage gaps, dispositioning orphaned accounts, surfacing white space on top of clean parent IDs.
• Location-level precision — moving customers off monolithic HQ-level enrichment so geo-based sales teams see local firmographics instead of global rollups.
• Automated, no-human-in-the-loop logic — entity suppression, disposition-based matching, orchestration rules for inactive entities, parent linkages, and white space alerts.
• Data warehouse as the operating layer — moving sophisticated analysis out of CRM (Salesforce can't do hierarchy work at scale) into Snowflake or BigQuery, via API or data cube depending on the workflow.
• Buying group filtering — applying persona-density criteria across hierarchies to turn a customer's 5,600 Disney legal entities into 31 actionable targets.

Two recent engagements as concrete reference points:

A global infrastructure customer brought us 1.8M records and 300K flagged as "unmatched," with a data team of one. An automated domain validation pipeline reframed the problem entirely — 175K inactive sites, 30K redirects, 65K real opportunities — and turned a "coverage gap" conversation into a data quality program.

An enterprise planning platform had 120K Salesforce accounts, broken hierarchies, and field leaders reporting near-zero confidence in their enrichment data. The underlying data was accurate; the matching was wrong. Custom disposition logic on a 10K-account priority sample produced 7,444 high-confidence matches at 98–99% accuracy — validated before scaling the framework to the full universe.

This is the work. It moves between data engineering, applied product development, and stakeholder management — often in the same week.

What You'll Build

Every engagement comprises a consistent service architecture — three pillars built on top of each other, and five capability areas assembled into the actual deliverable.

Three pillars:

1. Data Foundation — golden reference matching, persistent IDs, unified entity profiles across the customer's first-party systems.
2. Data Management — business-specific logic that turns the foundation into something the customer can actually go to market with: customer definitions, account models, entity resolution.
3. Activation — TAM to SAM to SOM, fit scoring, in-market signals.

Five capability areas:

• Data Foundation Development — Match every record across the customer's CRM, ERP, billing, and marketing systems to a golden reference dataset. Build custom disposition logic, domain validation, marketability classification, and legal entity crosswalks. The output is a single persistent ID linking every system.
• Account Architecture & Entity Resolution — Define what an account means for the customer's business (address-based, country, HQ, ultimate-parent rollup, or hybrid), then build automated logic that enforces that structure: duplicate resolution, inactive entity disposition, hierarchy linkages.
• TAM Development & White Space Discovery — Build the customer's complete addressable market against ICP criteria, suppress against existing customers via ultimate-parent rollup, surface white space inside customer hierarchies, and apply buying group filters to reduce overwhelming entity volume to targeted pipeline.
• Account Fit Scoring & In-Market Signals — Build custom fit models from historical win/loss patterns; configure both evergreen signals (executive moves, leadership changes, funding rounds) and tailored ones (intent topic spikes, senior job postings, technology displacement events).
• Ongoing Governance & Automation — Match orchestration rules, enrichment segmentation, CRM field locking, and warehouse integration (Snowflake, BigQuery) that keep the foundation clean as the business evolves.

Most engagements use at least three of these. Your job is to diagnose which the customer actually needs, scope the work, and deliver.

What You'll Do

Own Strategic Customer Engagements End-to-End Serve as the primary technical point of contact for assigned strategic accounts. Run discovery sessions with data engineers, sales operations leaders, and revenue executives. Diagnose the underlying problem (which is rarely the one the customer first describes — "we have a coverage gap" often turns out to be a marketability problem, "we don't trust the data" often turns out to be a matching problem). Scope the use case, design the solution, build the application, deploy it in the customer's environment, and stay accountable for the outcome — not just the delivery.

Bridge Technical and Business Audiences Sit with the sales team. Present to executive leadership. Synthesize complex go-to-market data needs into clear, actionable proposals — then deliver the solution. You're equally comfortable whiteboarding matching architecture with a data engineering team, walking a sales operations director through disposition codes, and presenting ROI to a CRO.

Build the FDE Playbook Document what works: discovery frameworks, engagement phases, integration patterns, deliverable templates, success metrics. The current strategic-account engagements are reference patterns — your job is to extract what's repeatable from each new account and turn it into a model that scales to additional strategic accounts, verticals, and eventually a team. Feed field learnings back to product, engineering, and data teams to inform product direction and dataset priorities.

Drive Stickiness and Expansion Every application you build embeds ZoomInfo more deeply into the customer's daily workflows. Identify expansion opportunities as they emerge — new use cases, new personas, new datasets, displacement opportunities against incumbent providers.

What We're Looking For

High Ownership, High Ambiguity Tolerance This role doesn't exist yet at ZoomInfo. You take ownership of outcomes — not tasks — and you're comfortable making judgment calls with incomplete information, building process where there is none, and figuring things out as you go.

Strong Software and Data Engineering Fundamentals You write production-quality code. You're proficient in Python and SQL, and comfortable working in cloud data warehouses (Snowflake, BigQuery, Databricks, or similar). You've built or substantially worked with: data pipelines, entity matching or deduplication logic, API integrations, and applications that real users depend on daily. Familiarity with API tooling — GraphQL, REST, Postman, authentication patterns (JWT, OAuth) — is a plus; deep expertise isn't required, but you should be comfortable navigating and integrating against APIs quickly. You work fluently in LLM-based development environments like Claude Code or Codex — these are core tools in how we build, not a nice-to-have.

Customer-Facing Communication You've worked directly with customers in a technical capacity — solutions engineering, consulting, technical account management, or a previous FDE role. You can synthesize complex data needs for an executive audience and discuss matching architecture with a data engineering team in the same meeting. You're comfortable navigating enterprise environments with competing stakeholders — sales operations, IT, marketing ops, finance, and revenue leadership all sit at the same table.

Go-to-Market Data Familiarity (Preferred) Experience working with B2B data, CRM systems (Salesforce, HubSpot), enrichment and orchestration tooling (RingLead, Clearbit, Demandbase), or similar go-to-market infrastructure. Familiarity with concepts like firmographic enrichment, entity resolution, hierarchy management, TAM modeling, intent data, and account-based prospecting. This isn't required — if you're a fast learner with the right instincts, we'll get you there.

Why This Role

• The mandate is clear. Driving data consumption and growth across ZoomInfo's strategic accounts is a top company priority. We already have working prototypes, validated customer demand, and executive sponsorship. The data team, product team, and infrastructure are in place. What's needed is the person who executes.
• Enterprise customer access. ZoomInfo's customer base includes market-leading GTM organizations at some of the largest enterprises in the world. You'll be embedded with these teams, solving real problems with meaningful budgets and complex data needs.
• Best-in-class data and infrastructure. ZoomInfo's data foundation, GTM Data Store, query infrastructure, and vertical dataset catalog give you the raw materials to build custom intelligence applications that aren't possible anywhere else.

#LI-AR2

#LI-remote

About the Role

ZoomInfo is seeking a Senior Data Systems Analyst to become the expert on our company data pipeline—the system that ingests, processes, and profiles millions of company records that power our customers' go-to-market strategies.

In this role, you'll build deep expertise in how our company data flows from acquisition through profiling and output. You'll read code to understand data transformations and system dependencies, bring informed opinions to design conversations with Engineering and Product, and help shape the evolution of our next-generation data infrastructure. As you build mastery of our systems, you'll increasingly lead strategic data improvement initiatives that require both systems thinking and creative problem-solving.

This isn't about building dashboards or SQL reports. This is about understanding data systems at an architectural level, solving ambiguous data challenges, and ensuring our pipeline infrastructure continuously evolves to meet customer needs and maintain competitive advantage.

You'll work closely with other data analysts during an active infrastructure transition period, and as systems stabilize and your expertise deepens, you'll progressively own more of the pipeline architecture and strategic initiatives. This is a role with significant growth runway for someone who wants to become the go-to technical expert on company data systems.

Who You Are

Systems Thinker with Technical Depth: You understand how data systems work, not just what they produce. You've worked with data pipelines, ETL systems, or data processing infrastructure—maybe you've improved one, debugged one, or owned components of one. You can read code (Python, Java, SQL, or similar) well enough to understand data transformations and trace how data flows through systems.

Opinionated Technical Contributor: You don't just execute—you have informed opinions on how things should work. You can assess technical tradeoffs, evaluate whether a proposed solution is feasible, and contribute meaningfully to design conversations with engineers. You're comfortable saying "here's what I think and why" based on your technical understanding.

Growth-Oriented Problem Solver: You're excited to build deep expertise in a complex domain and grow into leading strategic initiatives. You've tackled ambiguous problems that required figuring things out as you went, and you want to expand your project leadership capabilities in a systems-focused environment.

Analytical and Hands-On: You're equally comfortable writing code to analyze data patterns and manually investigating edge cases to understand what's really happening. You dig into details when needed and know when to zoom out to see the bigger picture.

Clear Communicator: You can explain technical complexity to non-technical audiences. You've worked effectively with Engineering, Product, or cross-functional teams, translating between technical constraints and business needs.

Comfortable with Ambiguity: You thrive in evolving environments where priorities shift and problems aren't always well-defined. You maintain momentum and quality even when the path forward isn't perfectly clear.

What You'll Do

In your first 6-12 months, your primary focus will be building deep expertise in our pipeline architecture and contributing to our infrastructure transition. You'll work alongside other analysts who have context on our systems, learning the architecture while bringing fresh perspectives and technical depth. As you gain mastery and systems stabilize, you'll increasingly own pipeline architecture decisions and lead strategic data improvement initiatives.

Build Deep Pipeline & Systems Expertise

• Master our company data pipeline architecture—how data flows from ingestion through profiling, what transforms are applied at each stage, and how components interconnect
• Read and analyze production code to understand data transformations, trace data lineage, and assess how proposed changes would impact the system
• Develop frameworks for evaluating tradeoffs between technical complexity, implementation effort, and customer impact
• Create clear documentation, system maps, and knowledge resources that capture architecture decisions, dependencies, and design rationale

Contribute to Pipeline Evolution & Infrastructure Improvements

• Participate actively in design conversations with Engineering and Product about our next-generation pipeline, bringing data quality insights, technical feasibility assessments, and informed opinions on architectural decisions
• Help validate pipeline improvements through rigorous testing, impact analysis, and hands-on verification of data quality
• Translate data quality investigations and emerging requirements into system-level improvement opportunities
• Collaborate with team members to determine when problems should be solved at the pipeline/profiler level versus through downstream approaches

Solve Complex, Ambiguous Data Challenges

• Lead or contribute to data improvement initiatives that require both systems thinking and creative problem-solving—such as improving location verification across international markets, integrating new data sources, or solving novel data extraction challenges
• Tackle problems where the solution isn't obvious through a blend of code analysis, manual investigation, cross-functional coordination, and iterative problem-solving
• Build and apply repeatable approaches to testing, validation, and root cause analysis

Build Partnerships & Institutional Knowledge

• Develop strong working relationships with Data Acquisition, Product, Engineering, and fellow data analysts
• Conduct impact analyses and validation studies to ensure proposed changes deliver intended outcomes
• Document your learning, approaches, and insights so knowledge is shared and institutional memory builds across the team
• Serve as a technical resource as you develop expertise, helping bridge immediate data quality needs with long-term pipeline capabilities

What You'll Bring

Required

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field
• 5+ years of experience in data analytics, data engineering, or related technical roles
• Experience working with data pipelines, ETL systems, or data processing infrastructure—you understand how data moves through systems and what can go wrong
• Ability to read and understand code (Python, Java, SQL, or similar) to analyze data transformations, understand system logic, and assess technical feasibility
• Strong programming skills in Python and SQL for data analysis and manipulation
• Experience solving ambiguous, multi-faceted data problems that required figuring out the approach, not just executing a well-defined analysis
• Demonstrated ability to work effectively with Engineering and/or Product teams, translating between technical implementation and business/customer needs
• Strong analytical skills with ability to investigate complex issues systematically
• Excellent communication skills—able to explain technical concepts clearly to diverse audiences
• Self-directed with strong ownership mentality—you drive your work forward and know when to seek input

Strongly Preferred

• Experience with company data, business data, web data acquisition, or data quality initiatives
• Experience with data profiling, entity resolution, record linkage, or data matching systems
• Background contributing to system design discussions or technical architecture decisions
• Experience creating technical documentation or knowledge-sharing resources
• Knowledge of web data structures (HTML/DOM), data extraction, or data collection systems
• Experience designing validation strategies or testing frameworks
• Track record of leading or co-leading cross-functional data projects

This Role Is Not a Fit If:

You're looking to focus primarily on business analytics, building dashboards, creating reports, or analyzing business metrics. While you'll do analysis in this role, the core mission is understanding and improving data systems architecture.

This Role Is a Strong Fit If:

You want to become the expert on how data systems work and grow into leading strategic initiatives. You're excited to build deep technical expertise, contribute informed opinions to design decisions, and progressively take on more ownership as you master the domain. You enjoy solving problems where the answer isn't obvious and seeing your work directly improve product capabilities.

#LI-JH1

#LI-Hybrid

About the Role

ZoomInfo is seeking a Senior Data Systems Analyst to become the expert on our company data pipeline—the system that ingests, processes, and profiles millions of company records that power our customers' go-to-market strategies.

In this role, you'll build deep expertise in how our company data flows from acquisition through profiling and output. You'll read code to understand data transformations and system dependencies, bring informed opinions to design conversations with Engineering and Product, and help shape the evolution of our next-generation data infrastructure. As you build mastery of our systems, you'll increasingly lead strategic data improvement initiatives that require both systems thinking and creative problem-solving.

This isn't about building dashboards or SQL reports. This is about understanding data systems at an architectural level, solving ambiguous data challenges, and ensuring our pipeline infrastructure continuously evolves to meet customer needs and maintain competitive advantage.

You'll work closely with other data analysts during an active infrastructure transition period, and as systems stabilize and your expertise deepens, you'll progressively own more of the pipeline architecture and strategic initiatives. This is a role with significant growth runway for someone who wants to become the go-to technical expert on company data systems.

Who You Are

Systems Thinker with Technical Depth: You understand how data systems work, not just what they produce. You've worked with data pipelines, ETL systems, or data processing infrastructure—maybe you've improved one, debugged one, or owned components of one. You can read code (Python, Java, SQL, or similar) well enough to understand data transformations and trace how data flows through systems.

Opinionated Technical Contributor: You don't just execute—you have informed opinions on how things should work. You can assess technical tradeoffs, evaluate whether a proposed solution is feasible, and contribute meaningfully to design conversations with engineers. You're comfortable saying "here's what I think and why" based on your technical understanding.

Growth-Oriented Problem Solver: You're excited to build deep expertise in a complex domain and grow into leading strategic initiatives. You've tackled ambiguous problems that required figuring things out as you went, and you want to expand your project leadership capabilities in a systems-focused environment.

Analytical and Hands-On: You're equally comfortable writing code to analyze data patterns and manually investigating edge cases to understand what's really happening. You dig into details when needed and know when to zoom out to see the bigger picture.

Clear Communicator: You can explain technical complexity to non-technical audiences. You've worked effectively with Engineering, Product, or cross-functional teams, translating between technical constraints and business needs.

Comfortable with Ambiguity: You thrive in evolving environments where priorities shift and problems aren't always well-defined. You maintain momentum and quality even when the path forward isn't perfectly clear.

What You'll Do

In your first 6-12 months, your primary focus will be building deep expertise in our pipeline architecture and contributing to our infrastructure transition. You'll work alongside other analysts who have context on our systems, learning the architecture while bringing fresh perspectives and technical depth. As you gain mastery and systems stabilize, you'll increasingly own pipeline architecture decisions and lead strategic data improvement initiatives.

Build Deep Pipeline & Systems Expertise

• Master our company data pipeline architecture—how data flows from ingestion through profiling, what transforms are applied at each stage, and how components interconnect
• Read and analyze production code to understand data transformations, trace data lineage, and assess how proposed changes would impact the system
• Develop frameworks for evaluating tradeoffs between technical complexity, implementation effort, and customer impact
• Create clear documentation, system maps, and knowledge resources that capture architecture decisions, dependencies, and design rationale

Contribute to Pipeline Evolution & Infrastructure Improvements

• Participate actively in design conversations with Engineering and Product about our next-generation pipeline, bringing data quality insights, technical feasibility assessments, and informed opinions on architectural decisions
• Help validate pipeline improvements through rigorous testing, impact analysis, and hands-on verification of data quality
• Translate data quality investigations and emerging requirements into system-level improvement opportunities
• Collaborate with team members to determine when problems should be solved at the pipeline/profiler level versus through downstream approaches

Solve Complex, Ambiguous Data Challenges

• Lead or contribute to data improvement initiatives that require both systems thinking and creative problem-solving—such as improving location verification across international markets, integrating new data sources, or solving novel data extraction challenges
• Tackle problems where the solution isn't obvious through a blend of code analysis, manual investigation, cross-functional coordination, and iterative problem-solving
• Build and apply repeatable approaches to testing, validation, and root cause analysis

Build Partnerships & Institutional Knowledge

• Develop strong working relationships with Data Acquisition, Product, Engineering, and fellow data analysts
• Conduct impact analyses and validation studies to ensure proposed changes deliver intended outcomes
• Document your learning, approaches, and insights so knowledge is shared and institutional memory builds across the team
• Serve as a technical resource as you develop expertise, helping bridge immediate data quality needs with long-term pipeline capabilities

What You'll Bring

Required

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field
• 5+ years of experience in data analytics, data engineering, or related technical roles
• Experience working with data pipelines, ETL systems, or data processing infrastructure—you understand how data moves through systems and what can go wrong
• Ability to read and understand code (Python, Java, SQL, or similar) to analyze data transformations, understand system logic, and assess technical feasibility
• Strong programming skills in Python and SQL for data analysis and manipulation
• Experience solving ambiguous, multi-faceted data problems that required figuring out the approach, not just executing a well-defined analysis
• Demonstrated ability to work effectively with Engineering and/or Product teams, translating between technical implementation and business/customer needs
• Strong analytical skills with ability to investigate complex issues systematically
• Excellent communication skills—able to explain technical concepts clearly to diverse audiences
• Self-directed with strong ownership mentality—you drive your work forward and know when to seek input

Strongly Preferred

• Experience with company data, business data, web data acquisition, or data quality initiatives
• Experience with data profiling, entity resolution, record linkage, or data matching systems
• Background contributing to system design discussions or technical architecture decisions
• Experience creating technical documentation or knowledge-sharing resources
• Knowledge of web data structures (HTML/DOM), data extraction, or data collection systems
• Experience designing validation strategies or testing frameworks
• Track record of leading or co-leading cross-functional data projects

This Role Is Not a Fit If:

You're looking to focus primarily on business analytics, building dashboards, creating reports, or analyzing business metrics. While you'll do analysis in this role, the core mission is understanding and improving data systems architecture.

This Role Is a Strong Fit If:

You want to become the expert on how data systems work and grow into leading strategic initiatives. You're excited to build deep technical expertise, contribute informed opinions to design decisions, and progressively take on more ownership as you master the domain. You enjoy solving problems where the answer isn't obvious and seeing your work directly improve product capabilities.

#LI-JH1

#LI-Hybrid

About the Role

ZoomInfo is seeking a Senior Data Systems Analyst to become the expert on our company data pipeline—the system that ingests, processes, and profiles millions of company records that power our customers' go-to-market strategies.

In this role, you'll build deep expertise in how our company data flows from acquisition through profiling and output. You'll read code to understand data transformations and system dependencies, bring informed opinions to design conversations with Engineering and Product, and help shape the evolution of our next-generation data infrastructure. As you build mastery of our systems, you'll increasingly lead strategic data improvement initiatives that require both systems thinking and creative problem-solving.

This isn't about building dashboards or SQL reports. This is about understanding data systems at an architectural level, solving ambiguous data challenges, and ensuring our pipeline infrastructure continuously evolves to meet customer needs and maintain competitive advantage.

You'll work closely with other data analysts during an active infrastructure transition period, and as systems stabilize and your expertise deepens, you'll progressively own more of the pipeline architecture and strategic initiatives. This is a role with significant growth runway for someone who wants to become the go-to technical expert on company data systems.

Who You Are

Systems Thinker with Technical Depth: You understand how data systems work, not just what they produce. You've worked with data pipelines, ETL systems, or data processing infrastructure—maybe you've improved one, debugged one, or owned components of one. You can read code (Python, Java, SQL, or similar) well enough to understand data transformations and trace how data flows through systems.

Opinionated Technical Contributor: You don't just execute—you have informed opinions on how things should work. You can assess technical tradeoffs, evaluate whether a proposed solution is feasible, and contribute meaningfully to design conversations with engineers. You're comfortable saying "here's what I think and why" based on your technical understanding.

Growth-Oriented Problem Solver: You're excited to build deep expertise in a complex domain and grow into leading strategic initiatives. You've tackled ambiguous problems that required figuring things out as you went, and you want to expand your project leadership capabilities in a systems-focused environment.

Analytical and Hands-On: You're equally comfortable writing code to analyze data patterns and manually investigating edge cases to understand what's really happening. You dig into details when needed and know when to zoom out to see the bigger picture.

Clear Communicator: You can explain technical complexity to non-technical audiences. You've worked effectively with Engineering, Product, or cross-functional teams, translating between technical constraints and business needs.

Comfortable with Ambiguity: You thrive in evolving environments where priorities shift and problems aren't always well-defined. You maintain momentum and quality even when the path forward isn't perfectly clear.

What You'll Do

In your first 6-12 months, your primary focus will be building deep expertise in our pipeline architecture and contributing to our infrastructure transition. You'll work alongside other analysts who have context on our systems, learning the architecture while bringing fresh perspectives and technical depth. As you gain mastery and systems stabilize, you'll increasingly own pipeline architecture decisions and lead strategic data improvement initiatives.

Build Deep Pipeline & Systems Expertise

• Master our company data pipeline architecture—how data flows from ingestion through profiling, what transforms are applied at each stage, and how components interconnect
• Read and analyze production code to understand data transformations, trace data lineage, and assess how proposed changes would impact the system
• Develop frameworks for evaluating tradeoffs between technical complexity, implementation effort, and customer impact
• Create clear documentation, system maps, and knowledge resources that capture architecture decisions, dependencies, and design rationale

Contribute to Pipeline Evolution & Infrastructure Improvements

• Participate actively in design conversations with Engineering and Product about our next-generation pipeline, bringing data quality insights, technical feasibility assessments, and informed opinions on architectural decisions
• Help validate pipeline improvements through rigorous testing, impact analysis, and hands-on verification of data quality
• Translate data quality investigations and emerging requirements into system-level improvement opportunities
• Collaborate with team members to determine when problems should be solved at the pipeline/profiler level versus through downstream approaches

Solve Complex, Ambiguous Data Challenges

• Lead or contribute to data improvement initiatives that require both systems thinking and creative problem-solving—such as improving location verification across international markets, integrating new data sources, or solving novel data extraction challenges
• Tackle problems where the solution isn't obvious through a blend of code analysis, manual investigation, cross-functional coordination, and iterative problem-solving
• Build and apply repeatable approaches to testing, validation, and root cause analysis

Build Partnerships & Institutional Knowledge

• Develop strong working relationships with Data Acquisition, Product, Engineering, and fellow data analysts
• Conduct impact analyses and validation studies to ensure proposed changes deliver intended outcomes
• Document your learning, approaches, and insights so knowledge is shared and institutional memory builds across the team
• Serve as a technical resource as you develop expertise, helping bridge immediate data quality needs with long-term pipeline capabilities

What You'll Bring

Required

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field
• 5+ years of experience in data analytics, data engineering, or related technical roles
• Experience working with data pipelines, ETL systems, or data processing infrastructure—you understand how data moves through systems and what can go wrong
• Ability to read and understand code (Python, Java, SQL, or similar) to analyze data transformations, understand system logic, and assess technical feasibility
• Strong programming skills in Python and SQL for data analysis and manipulation
• Experience solving ambiguous, multi-faceted data problems that required figuring out the approach, not just executing a well-defined analysis
• Demonstrated ability to work effectively with Engineering and/or Product teams, translating between technical implementation and business/customer needs
• Strong analytical skills with ability to investigate complex issues systematically
• Excellent communication skills—able to explain technical concepts clearly to diverse audiences
• Self-directed with strong ownership mentality—you drive your work forward and know when to seek input

Strongly Preferred

• Experience with company data, business data, web data acquisition, or data quality initiatives
• Experience with data profiling, entity resolution, record linkage, or data matching systems
• Background contributing to system design discussions or technical architecture decisions
• Experience creating technical documentation or knowledge-sharing resources
• Knowledge of web data structures (HTML/DOM), data extraction, or data collection systems
• Experience designing validation strategies or testing frameworks
• Track record of leading or co-leading cross-functional data projects

This Role Is Not a Fit If:

You're looking to focus primarily on business analytics, building dashboards, creating reports, or analyzing business metrics. While you'll do analysis in this role, the core mission is understanding and improving data systems architecture.

This Role Is a Strong Fit If:

You want to become the expert on how data systems work and grow into leading strategic initiatives. You're excited to build deep technical expertise, contribute informed opinions to design decisions, and progressively take on more ownership as you master the domain. You enjoy solving problems where the answer isn't obvious and seeing your work directly improve product capabilities.

#LI-JH1

#LI-Hybrid

About the Role

ZoomInfo is seeking a Senior Data Systems Analyst to become the expert on our company data pipeline—the system that ingests, processes, and profiles millions of company records that power our customers' go-to-market strategies.

In this role, you'll build deep expertise in how our company data flows from acquisition through profiling and output. You'll read code to understand data transformations and system dependencies, bring informed opinions to design conversations with Engineering and Product, and help shape the evolution of our next-generation data infrastructure. As you build mastery of our systems, you'll increasingly lead strategic data improvement initiatives that require both systems thinking and creative problem-solving.

This isn't about building dashboards or SQL reports. This is about understanding data systems at an architectural level, solving ambiguous data challenges, and ensuring our pipeline infrastructure continuously evolves to meet customer needs and maintain competitive advantage.

You'll work closely with other data analysts during an active infrastructure transition period, and as systems stabilize and your expertise deepens, you'll progressively own more of the pipeline architecture and strategic initiatives. This is a role with significant growth runway for someone who wants to become the go-to technical expert on company data systems.

Who You Are

Systems Thinker with Technical Depth: You understand how data systems work, not just what they produce. You've worked with data pipelines, ETL systems, or data processing infrastructure—maybe you've improved one, debugged one, or owned components of one. You can read code (Python, Java, SQL, or similar) well enough to understand data transformations and trace how data flows through systems.

Opinionated Technical Contributor: You don't just execute—you have informed opinions on how things should work. You can assess technical tradeoffs, evaluate whether a proposed solution is feasible, and contribute meaningfully to design conversations with engineers. You're comfortable saying "here's what I think and why" based on your technical understanding.

Growth-Oriented Problem Solver: You're excited to build deep expertise in a complex domain and grow into leading strategic initiatives. You've tackled ambiguous problems that required figuring things out as you went, and you want to expand your project leadership capabilities in a systems-focused environment.

Analytical and Hands-On: You're equally comfortable writing code to analyze data patterns and manually investigating edge cases to understand what's really happening. You dig into details when needed and know when to zoom out to see the bigger picture.

Clear Communicator: You can explain technical complexity to non-technical audiences. You've worked effectively with Engineering, Product, or cross-functional teams, translating between technical constraints and business needs.

Comfortable with Ambiguity: You thrive in evolving environments where priorities shift and problems aren't always well-defined. You maintain momentum and quality even when the path forward isn't perfectly clear.

What You'll Do

In your first 6-12 months, your primary focus will be building deep expertise in our pipeline architecture and contributing to our infrastructure transition. You'll work alongside other analysts who have context on our systems, learning the architecture while bringing fresh perspectives and technical depth. As you gain mastery and systems stabilize, you'll increasingly own pipeline architecture decisions and lead strategic data improvement initiatives.

Build Deep Pipeline & Systems Expertise

• Master our company data pipeline architecture—how data flows from ingestion through profiling, what transforms are applied at each stage, and how components interconnect
• Read and analyze production code to understand data transformations, trace data lineage, and assess how proposed changes would impact the system
• Develop frameworks for evaluating tradeoffs between technical complexity, implementation effort, and customer impact
• Create clear documentation, system maps, and knowledge resources that capture architecture decisions, dependencies, and design rationale

Contribute to Pipeline Evolution & Infrastructure Improvements

• Participate actively in design conversations with Engineering and Product about our next-generation pipeline, bringing data quality insights, technical feasibility assessments, and informed opinions on architectural decisions
• Help validate pipeline improvements through rigorous testing, impact analysis, and hands-on verification of data quality
• Translate data quality investigations and emerging requirements into system-level improvement opportunities
• Collaborate with team members to determine when problems should be solved at the pipeline/profiler level versus through downstream approaches

Solve Complex, Ambiguous Data Challenges

• Lead or contribute to data improvement initiatives that require both systems thinking and creative problem-solving—such as improving location verification across international markets, integrating new data sources, or solving novel data extraction challenges
• Tackle problems where the solution isn't obvious through a blend of code analysis, manual investigation, cross-functional coordination, and iterative problem-solving
• Build and apply repeatable approaches to testing, validation, and root cause analysis

Build Partnerships & Institutional Knowledge

• Develop strong working relationships with Data Acquisition, Product, Engineering, and fellow data analysts
• Conduct impact analyses and validation studies to ensure proposed changes deliver intended outcomes
• Document your learning, approaches, and insights so knowledge is shared and institutional memory builds across the team
• Serve as a technical resource as you develop expertise, helping bridge immediate data quality needs with long-term pipeline capabilities

What You'll Bring

Required

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field
• 5+ years of experience in data analytics, data engineering, or related technical roles
• Experience working with data pipelines, ETL systems, or data processing infrastructure—you understand how data moves through systems and what can go wrong
• Ability to read and understand code (Python, Java, SQL, or similar) to analyze data transformations, understand system logic, and assess technical feasibility
• Strong programming skills in Python and SQL for data analysis and manipulation
• Experience solving ambiguous, multi-faceted data problems that required figuring out the approach, not just executing a well-defined analysis
• Demonstrated ability to work effectively with Engineering and/or Product teams, translating between technical implementation and business/customer needs
• Strong analytical skills with ability to investigate complex issues systematically
• Excellent communication skills—able to explain technical concepts clearly to diverse audiences
• Self-directed with strong ownership mentality—you drive your work forward and know when to seek input

Strongly Preferred

• Experience with company data, business data, web data acquisition, or data quality initiatives
• Experience with data profiling, entity resolution, record linkage, or data matching systems
• Background contributing to system design discussions or technical architecture decisions
• Experience creating technical documentation or knowledge-sharing resources
• Knowledge of web data structures (HTML/DOM), data extraction, or data collection systems
• Experience designing validation strategies or testing frameworks
• Track record of leading or co-leading cross-functional data projects

This Role Is Not a Fit If:

You're looking to focus primarily on business analytics, building dashboards, creating reports, or analyzing business metrics. While you'll do analysis in this role, the core mission is understanding and improving data systems architecture.

This Role Is a Strong Fit If:

You want to become the expert on how data systems work and grow into leading strategic initiatives. You're excited to build deep technical expertise, contribute informed opinions to design decisions, and progressively take on more ownership as you master the domain. You enjoy solving problems where the answer isn't obvious and seeing your work directly improve product capabilities.

#LI-JH1

#LI-Hybrid

About the Role

ZoomInfo is seeking a Senior Data Systems Analyst to become the expert on our company data pipeline—the system that ingests, processes, and profiles millions of company records that power our customers' go-to-market strategies.

In this role, you'll build deep expertise in how our company data flows from acquisition through profiling and output. You'll read code to understand data transformations and system dependencies, bring informed opinions to design conversations with Engineering and Product, and help shape the evolution of our next-generation data infrastructure. As you build mastery of our systems, you'll increasingly lead strategic data improvement initiatives that require both systems thinking and creative problem-solving.

This isn't about building dashboards or SQL reports. This is about understanding data systems at an architectural level, solving ambiguous data challenges, and ensuring our pipeline infrastructure continuously evolves to meet customer needs and maintain competitive advantage.

You'll work closely with other data analysts during an active infrastructure transition period, and as systems stabilize and your expertise deepens, you'll progressively own more of the pipeline architecture and strategic initiatives. This is a role with significant growth runway for someone who wants to become the go-to technical expert on company data systems.

Who You Are

Systems Thinker with Technical Depth: You understand how data systems work, not just what they produce. You've worked with data pipelines, ETL systems, or data processing infrastructure—maybe you've improved one, debugged one, or owned components of one. You can read code (Python, Java, SQL, or similar) well enough to understand data transformations and trace how data flows through systems.

Opinionated Technical Contributor: You don't just execute—you have informed opinions on how things should work. You can assess technical tradeoffs, evaluate whether a proposed solution is feasible, and contribute meaningfully to design conversations with engineers. You're comfortable saying "here's what I think and why" based on your technical understanding.

Growth-Oriented Problem Solver: You're excited to build deep expertise in a complex domain and grow into leading strategic initiatives. You've tackled ambiguous problems that required figuring things out as you went, and you want to expand your project leadership capabilities in a systems-focused environment.

Analytical and Hands-On: You're equally comfortable writing code to analyze data patterns and manually investigating edge cases to understand what's really happening. You dig into details when needed and know when to zoom out to see the bigger picture.

Clear Communicator: You can explain technical complexity to non-technical audiences. You've worked effectively with Engineering, Product, or cross-functional teams, translating between technical constraints and business needs.

Comfortable with Ambiguity: You thrive in evolving environments where priorities shift and problems aren't always well-defined. You maintain momentum and quality even when the path forward isn't perfectly clear.

What You'll Do

In your first 6-12 months, your primary focus will be building deep expertise in our pipeline architecture and contributing to our infrastructure transition. You'll work alongside other analysts who have context on our systems, learning the architecture while bringing fresh perspectives and technical depth. As you gain mastery and systems stabilize, you'll increasingly own pipeline architecture decisions and lead strategic data improvement initiatives.

Build Deep Pipeline & Systems Expertise

• Master our company data pipeline architecture—how data flows from ingestion through profiling, what transforms are applied at each stage, and how components interconnect
• Read and analyze production code to understand data transformations, trace data lineage, and assess how proposed changes would impact the system
• Develop frameworks for evaluating tradeoffs between technical complexity, implementation effort, and customer impact
• Create clear documentation, system maps, and knowledge resources that capture architecture decisions, dependencies, and design rationale

Contribute to Pipeline Evolution & Infrastructure Improvements

• Participate actively in design conversations with Engineering and Product about our next-generation pipeline, bringing data quality insights, technical feasibility assessments, and informed opinions on architectural decisions
• Help validate pipeline improvements through rigorous testing, impact analysis, and hands-on verification of data quality
• Translate data quality investigations and emerging requirements into system-level improvement opportunities
• Collaborate with team members to determine when problems should be solved at the pipeline/profiler level versus through downstream approaches

Solve Complex, Ambiguous Data Challenges

• Lead or contribute to data improvement initiatives that require both systems thinking and creative problem-solving—such as improving location verification across international markets, integrating new data sources, or solving novel data extraction challenges
• Tackle problems where the solution isn't obvious through a blend of code analysis, manual investigation, cross-functional coordination, and iterative problem-solving
• Build and apply repeatable approaches to testing, validation, and root cause analysis

Build Partnerships & Institutional Knowledge

• Develop strong working relationships with Data Acquisition, Product, Engineering, and fellow data analysts
• Conduct impact analyses and validation studies to ensure proposed changes deliver intended outcomes
• Document your learning, approaches, and insights so knowledge is shared and institutional memory builds across the team
• Serve as a technical resource as you develop expertise, helping bridge immediate data quality needs with long-term pipeline capabilities

What You'll Bring

Required

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related quantitative field
• 5+ years of experience in data analytics, data engineering, or related technical roles
• Experience working with data pipelines, ETL systems, or data processing infrastructure—you understand how data moves through systems and what can go wrong
• Ability to read and understand code (Python, Java, SQL, or similar) to analyze data transformations, understand system logic, and assess technical feasibility
• Strong programming skills in Python and SQL for data analysis and manipulation
• Experience solving ambiguous, multi-faceted data problems that required figuring out the approach, not just executing a well-defined analysis
• Demonstrated ability to work effectively with Engineering and/or Product teams, translating between technical implementation and business/customer needs
• Strong analytical skills with ability to investigate complex issues systematically
• Excellent communication skills—able to explain technical concepts clearly to diverse audiences
• Self-directed with strong ownership mentality—you drive your work forward and know when to seek input

Strongly Preferred

• Experience with company data, business data, web data acquisition, or data quality initiatives
• Experience with data profiling, entity resolution, record linkage, or data matching systems
• Background contributing to system design discussions or technical architecture decisions
• Experience creating technical documentation or knowledge-sharing resources
• Knowledge of web data structures (HTML/DOM), data extraction, or data collection systems
• Experience designing validation strategies or testing frameworks
• Track record of leading or co-leading cross-functional data projects

This Role Is Not a Fit If:

You're looking to focus primarily on business analytics, building dashboards, creating reports, or analyzing business metrics. While you'll do analysis in this role, the core mission is understanding and improving data systems architecture.

This Role Is a Strong Fit If:

You want to become the expert on how data systems work and grow into leading strategic initiatives. You're excited to build deep technical expertise, contribute informed opinions to design decisions, and progressively take on more ownership as you master the domain. You enjoy solving problems where the answer isn't obvious and seeing your work directly improve product capabilities.

#LI-JH1

#LI-Hybrid