Data Analytics Engineer

San José, Provincia de San José

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Function Tech Team Data/Artificial Intelligence (AI) Role Type Permanent Work Location Remote Date Posted 11/10/2026

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Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.

About the Role

Reporting directly to the Director, Data Analytics, the Data Analytics Engineer contributes to the technical design, development, and enhancement of Corporate Metrics data products at Veeam, including Annual Recurring Revenue (ARR). The role implements the technical roadmap set by the Director for these data assets to drive data-driven decision making across the organization. The ideal candidate brings strong data architecture and dimensional modeling expertise, clear communication, strategic thinking, and the ability to translate business needs into scalable, reliable, and well-governed data assets.

This role is integral to the transformation of Veeam’s Corporate Metrics, which support both internal and external reporting and analytics. Responsibilities include architecture and technical design, building dimensional data models, understanding business processes, and driving execution to deliver accurate and scalable solutions aligned to a roadmap. The Data Analytics Engineer implements team processes and collaborates with partners and business stakeholders to translate requirements.

What You’ll Do

Maintain Corporate Metrics Data Assets: Create and maintain the data pipelines, models, and storage solutions behind the Corporate Metrics data mart, including ARR, ensuring completeness, accuracy, and security.

Corporate Metrics Semantic Layer: Build out a robust semantic layer enabling business users to access corporate metrics through AI Tools in natural language.

Dimensional Data Modeling: Design and maintain dimensional models (star and snowflake schemas, conformed dimensions, fact tables, slowly changing dimensions) that serve as a consistent, reusable foundation for reporting and analytics across metric domains.

Data Architecture & Transformation Job Development: Design and implement robust, scalable, and efficient Databricks jobs to support sources of truth for Corporate Metrics reporting and analytics.

Data Quality & Governance: Collaborate and communicate requirements with the Data Quality and Governance team around data lineage, segmentation and sources of truth for Corporate Metrics data assets.

Collaboration: Work closely with the Central Data Engineering, Revenue Intelligence, and Corporate Technology teams to align on metric definitions, requirements, and reporting needs.

Communication: Act as the technical point of contact for Corporate Metrics data assets. Help translate business requirements into technical specs.

Continuous Improvement: Identify opportunities to improve data processes, automation, and reporting capabilities.

Release Management: Manage app changes through source control (e.g., GitHub or Azure DevOps), code review, and testing, following change processes that meet SOX and other compliance requirements.

Key Success Factors

Understanding of Business Strategy and Processes related to Corporate Metrics:

Grasp the definitions, calculations, and business impact of Corporate Metrics such as ARR and related SaaS, Sales, Finance, and Headcount metrics.

Align data processes with business requirements and financial reporting standards.

Technical Excellence:

Demonstrate strong skills in data architecture, dimensional modeling, pipeline development, and integration.

Ensure data quality, reliability, and scalability across all Corporate Metrics data systems.

Take full responsibility for the lifecycle of Corporate Metrics sources of truth in Databricks: design, development, release, support, and troubleshooting.

Use AI-assisted development responsibly, reviewing and testing generated code before release.

Ownership & Accountability:

Maintain the end-to-end lifecycle of Corporate Metrics data assets: design, implementation, maintenance, and troubleshooting.

Proactively address issues, risks, and gaps in the data.

Innovation & Continuous Improvement:

Stay informed about advancements in analytics engineering, data modeling, cloud technologies, and AI-assisted development.

Seek opportunities to automate, optimize, and enhance data processes.

Security & Compliance:

Ensure Corporate Metrics data assets and change processes comply with relevant security standards and regulations (e.g., SOX, GDPR)

What You’ll Bring

Bachelor’s or master’s degree in Business Analytics, Computer Science, Engineering or a related quantitative field.

3+ years of experience in data analytics or analytics engineering, with significant experience on corporate reporting data projects.

Strong SQL skills (DDL and DML)

Hands-on experience designing and building dimensional data models (Kimball methodology, star schemas, fact and dimension tables, slowly changing dimensions).

Proficiency with modern data engineering tools and platforms (e.g., Python, SQL, Databricks).

Experience building semantic layers or metric stores (e.g., Databricks Metric Views/Genie, dbt Semantic Layer) and exposing data to AI or natural-language tools.

Familiarity with DevOps processes and tools like Github or Azure DevOps

Understanding of ARR and other SaaS and corporate metrics.

Strong analytical skills and experience with AI-driven solutions.

Excellent communication and stakeholder management abilities.

Experience with external reporting needs for a public company.

Bonus Skills

Familiarity with Claude Code, ChatGPT, or other AI/ML-driven development tools.

Experience with Salesforce and Data Warehouse migrations or integrations.

Knowledge of automated data quality testing frameworks

What You’ll Get

Unlimited paid time off, plus 3 global VeeaMe Days for self-care
Paid parental leave: 8 weeks for all parents, 16 weeks for birthing parents
Medical, dental, and vision coverage from day one
Mental health support, therapy sessions, and digital wellness tools via SupportLinc EAP
401(k) retirement plan with matching contributions up to annual limits
Fertility, adoption, and surrogacy support through Maven, plus paid volunteer time
AirVet: 24/7 virtual veterinary care at no cost
Legal services, identity protection, and supplemental health insurance options
Tax-advantaged spending accounts for healthcare, dependent care, and commuting
Professional training and education, including courses and workshops, internal meetups, and unlimited access to our online learning platforms (LinkedIn Learning, Athena, O’Reilly) and mentoring through our MentorLab program

Please note: The position is based in San Jose, Costa Rica. If the applicant is permanently located outside of Costa Rica, Veeam reserves the right to decline the application. All applications must be submitted in English.

Veeam Software is an equal opportunity employer and does not tolerate discrimination in any form on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state or local law. All your information will be kept confidential.

Personal data collected during the recruitment process will be processed in accordance with our Recruiting Privacy Notice, which explains how your information is collected, used, and handled in connection with hiring activities. By applying for this position, you consent to this processing.

By submitting your application, you confirm that the information provided, including any supporting documents, is complete and accurate to the best of your knowledge. Any misrepresentation, omission, or falsification may result in disqualification from consideration or, if discovered after employment begins, termination of employment.

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