Role Description We are seeking a data-driven Ai Engineer to join our team at a high-growth advertising technology company. This role focuses on scaling our reporting infrastructure for advertising performance and billing reconciliation, ensuring that financial and operational data is accurate, automated, and actionable. - Develop robust data pipelines, ensuring data quality and reliability. - Enable efficient data consumption across the organization. - Collaborate closely with cross-functional teams including Product, Engineering, Analytics, and Business stakeholders to deliver high-impact data platforms. The ideal candidate is a proactive problem-solver with strong technical expertise, capable of working with large datasets, modern data architectures, and cloud-based environments. You thrive in fast-paced settings, navigate ambiguity with confidence, and are passionate about turning data into actionable value. Qualifications - Databricks & AI Architecture (Must-Have) - Strong experience working with Databricks Lakehouse architecture - Nice to have expertise in Databricks Mosaic AI and Unity Catalog for governing AI assets - Hands-on experience Building RAG (Retrieval-Augmented Generation) pipelines using Vector Search - SQL & Data Modeling (Must-Have) - Advanced SQL development - AI Engineering & Data Workflows (Must-Have) - Experience integrating LLM APIs (OpenAI, Anthropic, etc.) into data workflows - Hands-on experience using AI for: - Data enrichment - Anomaly detection - Automated classification - Experience with LangChain, LlamaIndex, or similar frameworks - Exposure to Model Context Protocol (MCP) or similar approaches to connect AI models with external tools and data sources - Strong understanding of Tool Calling / Function Calling: enabling LLMs to interact with SQL databases and external APIs securely. - Experience in Prompt Engineering and Guardrailing: designing system prompts that maintain context and hierarchy (e.g., understanding team associations). - Platform & Engineering Practices (Nice-to-Have / Medium) - Experience with GitHub workflows - Familiarity with CI/CD pipelines (Jenkins or similar) - Experience working with YAML/YML configuration files Requirements - Architect AI Agents: Build and deploy agents that can perform NLP-based data generation, automated data enrichment, and complex data reasoning within Databricks. - Natural Language Interfaces: Develop "Chat with your Data" features, allowing stakeholders to query the data warehouse using natural language. - Integrate LLMs into data workflows for automation and intelligence. - Develop scalable data models to support analytics and AI use cases. - Implement AI-driven enhancements such as anomaly detection and data enrichment. - Collaborate with data, analytics, and engineering teams to improve data reliability. - Optimize performance and scalability of data and AI workflows. - Support automation through CI/CD practices. - Ensure data quality, traceability, and maintainability across pipelines. Benefits - Monetary compensation - Year-end Bonus - IMSS, AFORE, INFONAVIT - Major Medical Expenses Insurance - Life Insurance - Funeral Expenses Coverage - TDU Membership - MediAccess - Health Check-Up Subsidy - Preferential rates for car insurance - Vacations - Official Mexican Holidays - Life Happens Days - Bereavement Leave - Civil Marriage Leave - English Classes - Certifications - Educational Agreements (Talisis, U-ERRE, UNID, TecMilenio, Tec de Monterrey, UDEM, SPIS) - Corporate Agreements & Discounts (Sorteos Tec, Envia Flores, TopGolf) - Taquitos Rewards - Birthday Bonus - Work-from-home Bonus - Laptop Policy

• Establish and evolve the enterprise data architecture vision aligned to Autodesk’s business strategy • Define enterprise data domains and ownership models across operational, customer, and analytical ecosystems • Develop guiding principles and guardrails for how data is created, shared, mastered, governed, and activated • Shape architectural patterns that support data products, domain-oriented ownership, and modern data paradigms • Identify enterprise data assets that can be activated to power AI/ML, personalization, automation, and intelligent decisioning • Design architectural patterns that make high-quality, governed data accessible for AI workflows across the organization • Ensure machine learning pipelines and AI systems are grounded in authoritative enterprise data domains • Partner with AI/ML and product teams to define scalable, reusable data foundations for experimentation and production AI systems • Remove systemic barriers that limit data discoverability, accessibility, and reuse • Create and maintain high-level conceptual and logical data models spanning CRM, ERP, Finance, subscription systems, marketing platforms, support systems, and analytics environments

Role Description We are seeking an experienced Senior Software Engineer, Data Platform, to contribute to the design, development, and optimization of our data infrastructure. This role requires deep expertise in GCP, data engineering, Change Data Capture (CDC), ETL, governance, and streaming technologies. You will work closely with data scientists, analysts, and software engineers to ensure seamless data ingestion, processing, and access across the organization. This is a foundational role within a growing team that requires a hands-on architect who can balance speed, excellence, and pragmatism. Key Responsibilities - Contribute to the design, development, and scaling of core data infrastructure using GCP, Spark, Databricks, and Fivetran. - Develop robust and maintainable ETL/ELT workflows that support diverse structured and unstructured data needs across the organization. - Implement and manage Change Data Capture (CDC) pipelines to enable near real-time data replication and synchronization. - Define and enforce data governance and compliance standards, including access control, auditability, lineage, and metadata management. - Build and manage streaming and batch data pipelines to serve high-impact use cases across analytics, product, compliance, and experimentation. - Act as a strategic partner to cross-functional teams (product, analytics, engineering, clinical) to ensure data is accessible, trustworthy, and impactful. - Drive the long-term architectural vision of our data platform to support current and future business and product needs. Qualifications - 5+ years of experience in software engineering, with a focus on scalable data architectures. - Strong expertise in GCP (IAM, GCS, Pub/Sub, etc.) and hands-on experience with Spark and Databricks. - Hands-on experience with CDC technologies like Fivetran, or equivalent. - Proficiency in ETL/ELT tools and frameworks (dbt, Apache Airflow, Dataform, etc.). - Deep understanding of data governance principles, including compliance and security best practices. - Demonstrated success in collaborating across functions to deliver data solutions for analytics, experimentation, or compliance. - A balance of IC execution and leadership skills; you’re equally comfortable rolling up your sleeves or mentoring others. - Familiarity with streaming data architecture, real-time ingestion, and delivery frameworks. - Proficient in SQL and Python for data processing and automation. - Strong problem-solving skills with the ability to work in a fast-paced environment. - Excellent communication and technical storytelling skills — you can align technical work with business value. Nice-to-Have Skills and Experiences - Experience with Terraform or Infrastructure-as-Code (IaC) for data infrastructure automation. - Background in HIPAA or other regulated environments with sensitivity to data privacy and compliance. - Familiarity with the dbt Semantic Layer and modern data modeling best practices. - Exposure to data observability platforms and practices. - Familiarity with machine learning data pipelines. - Exposure to multi-cloud or hybrid-cloud environments. - Experience building scalable solutions in a 0-1 environment. To be a strong fit, you also need - Bias Toward Action: Demonstrated ability to take initiative, make decisions under uncertainty, and move projects forward even in the face of ambiguity. - Entrepreneurial Spirit: Strong adaptability to changing business needs with a knack for building and optimizing processes. - Communication: Excellent communication skills, capable of explaining complex technical concepts to non-technical stakeholders. - Remote Work Adaptability: Comfort with remote work environments, demonstrating the ability to stay productive and connected with the team irrespective of physical location. - Continuous Improvement: A willingness to question assumptions and a commitment to continuous improvement. Benefits - Competitive salary. - Equity and benefits package. - Flexible, fully-remote working hours. - A dynamic work environment where creativity and innovation are encouraged.

• Lead and develop two Director-level managers and their respective teams spanning data platform engineering, data pipeline development, and practitioner enablement. • Set the multi-quarter roadmap for the Enterprise Data Platform and Engineering organizations, balancing infrastructure modernization, operational stability, security, and enablement priorities. • Build a high-performance engineering culture grounded in ownership, accountability, and operational excellence. • Drive headcount planning, organizational design, and career development frameworks that attract and retain top talent across a distributed team. • Serve as a trusted partner to the VP of Engineering & Innovation, contributing to broader engineering strategy and organizational decisions. • Establish clear prioritization frameworks to manage competing demands from security, infrastructure modernization, platform stability, and practitioner tooling. • Own the strategic direction for ZoomInfo's core data infrastructure, including Snowflake, Airflow, Fivetran, AWS, and GCP. • Guide the design and buildout of an Iceberg-based GCS Data Lake as the foundation for scalable, cost-efficient storage, including governance, observability, and ingestion patterns. • Oversee cloud infrastructure consolidation to GCP, including Airflow 3.x upgrades, service migrations, and deprecation of legacy environments. • Drive SaaS vendor strategy and cost optimization — evaluating build-vs-buy decisions for ingestion (Fivetran/Airbyte), cost management, and other platform tooling. • Oversee monorepo consolidation and CI/CD standardization to enable consistent governance and accelerate deployment velocity. • Ensure the platform team maintains a strong security posture including service account modernization, key rotation, legacy role decommissioning, access reviews, and incident response readiness. • Oversee the development, reliability, and monitoring of all enterprise data pipelines powering analytics, reporting, and operational workflows. • Guide the evolution of data modeling practices through dbt and semantic models, ensuring data products are trusted, documented, and well-tested. • Champion the shift-left initiative, enabling product and engineering domain teams to own their data assets end-to-end while maintaining quality and standards. • Support the development of AI-assisted tooling (agents and micro apps) that automate data engineering workflows — from ingestion and modeling to deployment and monitoring. • Drive platform-wide data standards alignment for metadata, ownership, testing, and documentation.