• Lead CPQ & Revenue Data Migrations • Own the data strategy for migrating core revenue entities including products, price books, quotes, quote line items, contracts, subscriptions, amendments, and invoice records. • Develop migration blueprints that support Salesforce CPQ and Billing data models, including multi-currency, multi-org, and tax configuration considerations. • Partner with functional teams to ensure accurate configuration of product bundles, pricing rules, and approval processes are reflected in data loads. • Define and document end-to-end data architecture, field mappings, and transformation logic across legacy ERP, CPQ tools, billing systems, and Salesforce. • Design flexible, reusable migration frameworks to accommodate ongoing data loads (delta or phased) across environments. • Ensure data migration aligns with Salesforce Revenue Cloud metadata structure and dependencies. • Build and execute SQL-based data extraction and transformation scripts using Microsoft SQL Server, DBAmp, or equivalent tools. • Perform iterative test loads to sandboxes and production environments, ensuring data quality and reconciliation with legacy systems. • Validate bundle integrity, pricing calculations, and downstream impacts on revenue recognition and reporting. • Conduct data migration discovery sessions and workshops with customers to refine scope and identify risks early. • Provide regular migration progress updates, issue logs, and cutover plans to project stakeholders. • Create a seamless customer experience through thorough documentation, testing, and enablement support. • Work closely with Salesforce Solution Architects, RevOps leaders, developers, and QA teams to ensure data integrity throughout the Quote-to-Cash lifecycle. • Contribute to standardization of data migration best practices, templates, and accelerators for Revenue Cloud engagements.

• Design and implement scalable data warehouse and lakehouse architectures on the Cloudera platform. • Define enterprise data models, governance frameworks, security standards, and data quality practices. • Architect and optimize analytics solutions across SQL engines including Impala, Hive, and Iceberg. • Design AI-powered analytics solutions leveraging LLMs, Retrieval-Augmented Generation (RAG), vector databases (such as PostgreSQL, Qdrant, Milvus), and NLQ capabilities. • Lead the integration of AI/ML capabilities into enterprise data platforms and data pipelines. • Leverage vibe coding / AI-assisted development tools to accelerate development and improve productivity. • Build and optimize batch and near real-time data pipelines. • Collaborate with business stakeholders to translate business requirements into scalable data products and analytics solutions. • Establish best practices for performance optimization, data architecture, and AI-assisted development. • Mentor teams on modern data architecture and AI-enabled development methodologies. • Ensure data security, governance, and compliance within enterprise data platforms.

Role Description The operations team is a highly strategic and analytical team that helps guide and implement strategic initiatives across the company. We are looking for a Data & Analytics specialist to connect and organize our data across the company to drive visibility into performance and strategy across our sales, marketing, product, operations, finance, and engineering efforts. The ideal candidate will have some background in data engineering. - Design, build and maintain business-critical data and distributed systems that will provide real time and reliable data to all of our go to market tools and internal users. - Connect our production backend/data to business systems including Salesforce, Marketo, Intercom, Google Analytics, Metabase, etc. This can include working with a data warehouse/data lake, organizing large scale data (we send 10 billion notifications a day), and building ETLs to business systems. - Evaluate ways to increase the efficiency of internal data flows and centralize sources of truth. - Innovate, design, and build data systems, services, and tools using GCP (Google Cloud Platform) that scale with OneSignal’s products and business requirements. - Work cross functionally including with the backend engineering team as well as business teams including operations, product, marketing, sales, customer success, support, finance, etc. - Analyze the data and create data insights and business insights to help move the business as well as assist and empower teams across the company in making data related decisions. - Build data science/machine learning models using internal and external data sources to identify potential new customers, those who are at risk of churn or those with potential upsell opportunities. - Work with Airflow, DBT, Presto, Hightouch and introduce the latest tools into our technology stack. Potentially, figure out how to incorporate artificial intelligence into our technology stack. Qualifications - 6+ years of professional experience in a technical area at a high growth startup is preferred. - Proficiency with Python and experience with DBT and Airflow is a plus. - Self driven and ability to identify problems and implement and identify solutions. - A combination of technical and business acumen. The ideal candidate would have an understanding of SaaS metrics and growth company infrastructure scaling challenges. - Strong interpersonal and communication skills and experience working cross functionally. - The ideal candidate has had experience growing and managing a smaller but high functioning team. Requirements - The New York and California base salary for this full time position is between $170,000 to $190,000. Your exact starting salary is determined by a number of factors such as your experience, skills, and qualifications. - In addition to base salary, we also offer a competitive equity program and comprehensive and inclusive benefits.

• Business Context Apoyar al equipo de Sostenibilidad de LATAM Airlines en la evolución de su plataforma de datos. El proyecto consiste en integrar y modelar distintas fuentes corporativas junto con la Base de Datos de Sostenibilidad, habilitando información confiable, trazable y escalable para análisis, reportabilidad y toma de decisiones.