• Create and maintain complex, enterprise-scale data pipelines and foundational datasets while defining technical strategy and architectural direction for advertising products • Design and build sophisticated ETL processes, data models, and analytical frameworks using SQL, Python, and modern data stack technologies • Build and maintain the data infrastructure that powers Ads ML - feature pipelines, label generation workflows, and training data systems that enable our ranking and delivery models • Develop data quality frameworks, monitoring systems, automated anomaly detection, and alerting infrastructure that operates at massive scale • Collaborate with data scientists, ML engineers, and product teams to identify high-impact data infrastructure opportunities, owning design through implementation • Drive cross-functional technical initiatives solving sophisticated data engineering challenges • Build scalable rubrics that help lead and mentor engineers through projects that accelerate launch velocity and harden data systems • Navigate ambiguity and make sound technical decisions with incomplete information, balancing short-term delivery with long-term infrastructure investment

• Design and lead data migration plans covering core Salesforce entities such as accounts, contacts, leads, opportunities, activities, cases, and custom objects. • Develop scalable data migration frameworks that support phased, delta, and full-load approaches across Salesforce environments. • Drive alignment of legacy data models to Salesforce architecture, considering business rules, relationships, and multi-org/multi-currency configurations. • Define canonical data models and develop field-level mapping and transformation logic from source systems to Salesforce. • Collaborate with MDM teams to establish golden record definitions and implement identity resolution strategies across systems. • Implement governance controls, data stewardship policies, and compliance mechanisms (e.g., GDPR, CCPA) to ensure trusted and compliant data usage. • Ensure data lineage and integrity across the CRM ecosystem by working closely with enterprise architects and data owners. • Conduct comprehensive data profiling, identify duplicates, incomplete records, and inconsistent values across datasets. • Lead deduplication efforts using tools such as Informatica Cloud Data Quality, DemandTools, or custom scripts. • Design rules and processes for data cleansing, normalization, and enrichment to ensure completeness and accuracy of migrated and integrated data. • Establish data quality KPIs and continuously monitor and report on data integrity across the Salesforce platform. • Develop and execute ETL processes using tools such as Informatica (Cloud and MDM), DBAmp, Pentaho, Dataloader, or SQL-based frameworks. • Manage iterative data loads into Salesforce sandboxes and production environments, validating against source system records and business rules. • Deliver thorough documentation, test plans, and support materials to enable stakeholder adoption and long-term maintenance. • Facilitate workshops with business stakeholders to define data requirements, risks, and success metrics. • Work closely with Salesforce Solution Architects, Admins, Developers, RevOps, and QA teams to ensure data supports end-to-end business processes. • Standardize data practices by contributing to internal playbooks, templates, accelerators, and knowledge sharing initiatives.

• Design, build, and maintain scalable data pipelines using Azure services (Data Factory, Synapse Pipelines, Databricks) • Implement data transformations and processing using Python, SQL, and Spark • Build and support data warehouse or lakehouse solutions in Azure Synapse or Microsoft Fabric • Lead data governance implementation using Microsoft Purview, including data cataloging, classification, lineage, and access policies • Support and advise clients on data governance frameworks, operating models, and best practices • Design and integrate Master Data Management (MDM) concepts, including reference data, golden records, and data ownership models • Partner with analytics, BI, and business stakeholders to deliver trusted, well-governed datasets • Monitor, optimize, and support data pipelines for performance, reliability, and cost

• Construir soluciones donde los datos fluyan de forma estructurada, confiable y escalable • Diseñar, desarrollar y mantener pipelines de datos, procesos de ingesta, transformación y orquestación en entornos de datos modernos • Colaborar con perfiles de plataforma, analítica, arquitectura y negocio