• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery • Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features • Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly • Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams • Improve data quality and reliability through testing, observability, alerting, and validation frameworks • Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions • Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions

Role Description We are looking for a Senior Data Engineer to design, build, and optimize scalable data platforms that support analytics, ecommerce, logistics, and marketing initiatives. This role will be responsible for developing robust data pipelines, improving the data infrastructure, and ensuring high-quality, reliable data solutions in an AWS environment. The ideal candidate has strong expertise in Python, SQL, Spark, Airflow, and cloud-based data architectures, along with excellent communication skills and a collaborative mindset. - Design, build, and maintain scalable data pipelines for analytics, ecommerce, logistics, and marketing. - Improve and maintain the company's data infrastructure and tooling, including Redshift/Snowflake, Airflow, and Fivetran. - Architect data processing systems capable of handling complex data flows and large-scale datasets. - Develop reliable, efficient, and cost-effective data solutions in an AWS environment. - Ensure high standards of data quality through automation and best engineering practices. - Optimize platform performance, resiliency, scalability, and operational costs. - Collaborate closely with software engineers, analysts, and business stakeholders. - Mentor engineers and analysts on data engineering best practices. - Participate in architecture discussions and technical decision-making. Qualifications - 4+ years of experience as a Data Engineer working with modern data technologies. - Strong proficiency in Python and SQL. - Experience processing large-scale datasets. - Hands-on experience with Apache Spark and Apache Airflow. - Experience designing and architecting data processing systems in AWS. - Experience with Redshift and/or Snowflake. - Knowledge of Fivetran or similar data ingestion tools. - Strong understanding of data quality, automation, and data engineering best practices. - Excellent communication and interpersonal skills. Requirements - Experience with streaming technologies such as Kafka or AWS Kinesis Firehose. - Knowledge of Docker and/or Kubernetes. - Familiarity with Ruby or R. - Experience building or migrating a Data Warehouse. - Experience leading projects or mentoring technical teams. Benefits - A stable, long-term contract with opportunities for career growth. - Private health insurance. - A remote-friendly culture that promotes work-life balance. - Continuous training, mentorship, and learning programs to keep you at the forefront of the industry. - Free access to AI training resources and state-of-the-art AI tools to elevate your daily work. - A flexible Paid Time Off (PTO) policy as well as paid holiday days. - Challenging, world-class software projects for clients in the US and LatAm. - Collaboration with some of the most talented software engineers in Latin America and the US, in a diverse work environment.

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 - Strong experience working with Databricks Lakehouse architecture (Must-Have). - 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. - Advanced SQL development (Must-Have). - Experience integrating LLM APIs (OpenAI, Anthropic, etc.) into data workflows (Must-Have). - 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. - Experience with GitHub workflows (Nice-to-Have / Medium). - 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. Company Description Enroute is committed to providing equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

• Propose and build: Design modern architectural solutions (focus on Data Lakehouse) and rapidly test new technologies. • Develop: Create efficient data processes and pipelines (ETL/ELT) using industry best practices. • Automate and Orchestrate: Implement automations in Cloud environments (GCP, AWS, or Azure), ensuring high performance, security, and data governance. • Operate with a Product Mindset: Ensure the data platform is scalable, flexible, and oriented to serve business areas with structured data and AI models. • Facilitate and Translate: Act as a bridge between technical and business teams, translating complex engineering and AI concepts for stakeholders in a clear and educational manner. • Collaborate: Support colleagues and teams in adopting industry best practices, serving as a technical reference.