• Advise our clients on introducing or expanding cloud- or on-premise-based Big Data architectures • Design requirement analyses and oversee implementation • Take ownership of end-to-end implementation, from data integration to use in analytical models and operationalization • Prepare and analyze large structured and unstructured datasets using scalable cloud infrastructures • Collaborate closely with Data Scientists and Data Engineers to develop analytical models, predictive analyses, and preventive countermeasures • Continuously propose improvements to increase the efficiency of deployed Big Data solutions • Expand existing client relationships, identify new projects, and independently acquire new customers.

Title: Staff Data Engineer Location: Boston United States Job Description: Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. About Toast At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement. The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations. Role Overview You'll be a founding member of a newly chartered data engineering function within Customer Success with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one. This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership. As a Data Engineer on the Customer Success Data & Analytics team, you'll design, build, and scale the data platforms and pipelines that power insights across our Customer Success organization. Reporting to the Director of Data Infrastructure & Engineering, you'll work closely with Data Infrastructure, Analytics, Engineering, and Product teams to ensure high-quality, reliable data is accessible and actionable. This is a hands-on engineering role focused on delivering production-grade, scalable, and reliable data systems that support analytics, reporting, and operational decision-making across Customer Success. A day in the life (Responsibilities) - Design, build, and maintain scalable, production-ready data pipelines (batch and real-time) for Customer Success using modern data technologies. - Develop and evolve data models and schemas that enable reporting, analytics, and machine learning across Customer Success functions. - Partner with CS operations leaders across care, workforce, and CX to translate business requirements into pipeline and infrastructure priorities, ensuring data work is directly connected to customer retention and operational outcomes. - Ensure data quality, observability, lineage, and governance are built into every data workflow. - Implement best practices for cost-efficient, performant cloud-based data infrastructure. - Contribute to data platform tooling and frameworks that support self-service analytics and reusability across teams. - Actively monitor and optimize data workflows for reliability, scale, and maintainability. - Collaborate with cross-functional stakeholders to define and track key metrics and ensure consistent data definitions. - Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards. - Build and maintain semantic and metrics layer foundations (e.g., dbt metrics, Snowflake semantic layer) that enable consistent, reusable definitions of CS KPIs across reporting and analytics surfaces. - Contribute to the design and evolution of the CS data domain model including Customer 360, omnichannel interaction data, case management, and agent performance in close partnership with cross-functional stakeholders. Governance & Standards Partner with Toast's Business, Technology & Transformation (BTT) organization to establish data governance practices, security policies, and architecture standards that will serve as the foundation for CS data infrastructure. As a founding team member, you'll help define the norms, not just follow them. - Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements. - Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations. - Maintain documentation and metadata for all data assets in accordance with established governance processes. What you'll need to thrive (Requirements) - 5+ years of experience in data engineering, software engineering, or a related role building and maintaining data systems in production. - Strong SQL skills and experience with a programming language such as Python, Scala, or Java. - Hands-on experience with cloud-based data platforms (AWS, GCP, or Azure). - Experience building and optimizing ETL/ELT workflows using tools such as Airflow, dbt, or similar orchestration frameworks. - Deep understanding of data modeling, data warehousing, and pipeline design. - Ability to write clean, maintainable, and well-tested code for data transformations and automation. - Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders. - Proven ability to work collaboratively with analytics, operations, and engineering teams. - Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems. What will help you stand out (Non-essential Skills/Nice to Haves) - Experience building and optimizing data systems that operate at significant scale, managing billions of records across multiple data domains and systems, with the foresight to design for 5-year growth and future platform scale. - Comfort using AI-assisted development tools (e.g., GitHub Copilot, Claude, Snowflake Cortex AI) to accelerate pipeline development, testing, and documentation and curiosity about how GenAI can enhance data product quality. - Experience with real-time data streaming (Kafka, Kinesis, or Pub/Sub). - Familiarity with modern data warehouse/lakehouse technologies (e.g., Snowflake, BigQuery, Redshift, Delta Lake). - Experience with CI/CD, infrastructure as code, or containerization (e.g., Docker, Terraform). - Exposure to data governance, metadata management, and observability tools. - Background in SaaS or Customer Success analytics (usage data, retention metrics, customer health). - Experience working with CS or CX-adjacent source systems such as Salesforce, Five9, Intercom, or similar CCaaS/CRM platforms and familiarity with the data models and integration patterns they produce. - Knowledge of data security, privacy, and compliance in cloud environments. AI at Toast At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it's a core part of our culture. Our Total Rewards Philosophy We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters' changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits. The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. You can learn more about how we align pay with local labor markets in our Geographic Pay Zone Philosophy. Pay Range $138,000-$221,000 USD How Toast Uses AI in its Hiring Process Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. Diversity, Equity, and Inclusion is Baked into our Recipe for Success At Toast, our employees are our secret ingredient-when they thrive, we thrive. The restaurant industry is one of the most diverse, and we embrace that diversity with authenticity, inclusivity, respect, and humility. By embedding these principles into our culture and design, we create equitable opportunities for all and raise the bar in delivering exceptional experiences. We Thrive Together We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the restaurant community. To learn more about how we work globally and regionally, check out: https://careers.toasttab.com/locations-toast. Apply today! Toast is committed to creating an accessible and inclusive hiring process. As part of this commitment, we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you need an accommodation to access the job application or interview process, please contact candidateaccommodations@toasttab.com. - ----- For roles in the United States, it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental. Job description: We are seeking an AI Data Engineer to design, build, and scale the data foundations that power AI, advanced analytics, and natural language interfaces across a global Quick Service Restaurant (QSR) enterprise. This is a high-impact role at the intersection of data engineering and applied AI, where the pipelines you build become the building blocks for use cases like automated insight generation, conversational analytics, and intelligent decision support at restaurant scale. A core focus of this role is data ingestion from POS systems and back-office vendor platforms spanning labor, inventory, finance, and supply chain. You will ensure reliable, standardized, and scalable ingestion of high-volume, multi-source data into the enterprise data platform, transforming fragmented operational data into AI-ready assets. You will also play a critical role in connecting raw data to business meaning, supporting governed KPI definitions and AI-driven context engineering through a robust semantic layer that enables both human analysts and AI agents to query, reason over, and act on trusted data. Key Responsibilities – Data Ingestion & Integration (POS & Back Office) - Design and build ingestion pipelines for POS systems across global markets (transactions, orders, payments, speed of service) - Integrate data from back-office vendor systems (labor scheduling, inventory, supply chain, finance) - Handle diverse ingestion patterns (batch, streaming, APIs, file-based ingestion) - Normalize and standardize data across vendors, regions, and brands - Ensure data freshness, completeness, and consistency across all ingestion pipelines - Manage vendor-specific schemas and evolving data contracts Key Responsibilities – Data Platform & Pipeline Engineering - Build scalable ELT/ETL pipelines using modern tools (DBT, Airflow, etc.) - Support real-time and batch processing for operational and analytical use cases - Optimize ingestion pipelines for performance, reliability, and cost - Ensure high availability across global data workloads Key Responsibilities – Semantic Layer Enablement - Build data models supporting standardized KPIs (same-store sales, speed of service, traffic, labor productivity) - Align ingested raw data with business-friendly semantic abstractions - Support both governed (manual) and AI-driven semantic approaches Key Responsibilities – Metadata & Context Engineering - Capture metadata, lineage, and schema details from ingestion pipelines - Enable AI systems using structured context (metadata, lineage, example queries) - Ensure AI systems can reliably interpret POS and operational data Key Responsibilities – Governance & Data Quality - Implement validation and monitoring for ingestion pipelines - Ensure data accuracy across POS and vendor systems - Support governance for consistent metric definitions across regions - Maintain versioning and schema evolution processes Required Qualifications - 5+ years of experience in data engineering - Strong SQL and Python skills - Experience building ingestion pipelines from APIs, streaming, and batch sources - Experience with POS or operational data systems preferred - Experience with cloud data platforms and modern data stack Preferred Qualifications - Familiarity with Agentic development tools such as Codex, Cursor or Claude Code - Experience in QSR or retail environments - Experience integrating vendor systems (labor, inventory, finance) - Familiarity with semantic layers and metadata tools - Experience enabling data for AI/LLM use cases Success Metrics - Reliability and latency of POS and back-office data ingestion - Standardization across vendor data sources - Reduction in data inconsistencies - Improved downstream analytics and AI performance Summary This role ensures that high-quality, standardized data from POS and back-office systems flows into the enterprise platform—enabling trusted analytics, semantic consistency, and AI-driven insights at global scale. Location: LATAM

• Extracting, cleansing, and loading data. • Building data pipelines using SQL, Kafka, and other technologies. • Investigate and troubleshoot issues with data and data pipelines. • Investigation and documentation of new data sets • Implement new data transformation configurations or ad-hoc reports • Triage incoming bugs and incidents. • Participation in sprint refinement, planning, and kick-off to help estimate stories, raise awareness and define additional implementation details. • Monitor data pipeline execution and performance. Resolve discovered issues collaboratively. • Perform and implement data quality checks to maintain consistent and accurate data.