• Define Technical Vision: Define the long-term data engineering strategy, factoring in company-wide priorities, customer needs, and system limitations. • Design Ecosystems: Create coherent designs across multiple pipelines and API boundaries. Reduce complex concepts to foundational components and simplify infrastructure to lower maintenance costs. • Drive Engineering Excellence: Make high-impact technical choices—including "build vs. buy" and framework selections—and provide clear rationale to rally the team. Review cross-team designs to preemptively identify and resolve technical risks before they jeopardize projects. • Improve Developer Efficiency: Implement solutions that measurably improve developer efficiency (e.g., cycle time, ramp-up time) and establish engineering-wide quality and best practices. • Deliver at Scale: Roll out major features and systems reliably, ensuring appropriate monitoring, failure domain characterization, and clear success metrics are established post-launch. • Align with Business Goals: Leverage a deep understanding of SmithRx’s business strategy to identify group-wide opportunities. Proactively refocus team efforts when projects are off-course or not moving the needle for the business. • Manage Data Quality & Governance: Enforce data governance policies (PII/PHI protection, security, compliance) and implement data quality principles to raise the bar for the reliability of data shared internally and externally. • Influence Cross-Functionally: Influence the roadmaps of other SmithRx teams. Act thoughtfully and decisively in critical situations, seeking diverse perspectives but ultimately leading decision-making (disagree and commit) to move priorities forward. • Mentor and Develop: Serve as a role model and coach for SDE2/SDE3 engineers, taking into account their unique skills and providing constructive feedback to maximize their impact. • Executive Communication: Develop focused messaging and effectively present technical strategies and business cases at the executive level. • Champion Organizational Change: Break down silos, build deep cross-functional relationships, and create excitement to drive the adoption of new technologies or processes across the organization.

• Interface to the data landscape: you integrate REST services and extract data from a wide range of sources. • Modern data engineering: you design and automate data pipelines and process and transform data using Apache Spark and Spark Notebooks (PySpark/SparkSQL). • Data modeling: you prepare data for analysis and take care of semantic modeling. • Customer focus: you analyze customer requirements and develop appropriate solutions together with the team. • Teamwork & community: you support junior consultants and working students, contribute to community projects, and share your knowledge, e.g. in blog posts.

• Recolectar datos de diversas fuentes, incluyendo bases de datos, archivos CSV y APIs, utilizando herramientas ETL para realizar limpieza y preprocesamiento. Eliminar valores atípicos, gestionar nulos y preparar datos para análisis eficiente. • Diseñar y construir pipelines de datos eficientes utilizando herramientas como Matillion, garantizando la automatización en la recolección, transformación y carga (ETL) de datos. • Integrar y consumir datos de APIs externas para enriquecer conjuntos de datos y mejorar la calidad de la información. • Contribuir al diseño de arquitecturas eficientes para el manejo de grandes volúmenes de datos, utilizando tecnologías como Python y SQL.

Location: Remote — Argentina, Colombia, México, Chile, Uruguay Job Type: Full-time contract Date Posted: March, 2026 Project Brief We're building a cloud-native data infrastructure powering AI-driven products — from ingestion pipelines and vector search to MCP-based integrations and multi-tenant architectures. You'll own the design, deployment, and reliability of data pipelines end-to-end, working across AWS, Terraform, and Pinecone in a fast-moving environment where architectural decisions matter and ownership is real. Must-Haves - Strong Python skills — Lambda functions, scripting, API consumption, and data processing pipelines - Hands-on AWS experience across: Lambda, S3, API Gateway, ECS/Fargate, Secrets Manager, Cognito, CloudWatch, and SQS + DLQ - Terraform for infrastructure-as-code — remote state with S3 + DynamoDB, modular resource definitions, and multi-environment management - Docker — containerization, Dockerfile hardening, and ECS task image builds - Pinecone — index creation, namespace design for multi-tenancy, metadata schema, upsert logic, and hybrid search configuration - Experience with embedding models and ETL/data transformation pipelines - MCP protocol experience — FastMCP SDK, Streamable HTTP transport, tool definition - REST API consumption — Cognito-authenticated requests, response parsing, and S3 persistence - Git/GitHub — trunk-based branching, semantic PRs, and branch-per-feature workflows - JSON schema design for pipeline service contracts and API response structures - Retry and error handling patterns — exponential backoff and failure architecture - Clear English communication and ability to work independently with minimal supervision Nice to Have - Incremental update and change detection logic for pipeline efficiency - CloudWatch alarm design and pipeline health dashboards - Content-hash deduplication for embedding cost optimization - Lambda memory and concurrency tuning - VPN site-to-site configuration - LangChain / LangGraph experience - Prompt engineering fundamentals - GitHub Actions — CI/CD pipeline setup and deployment automation - Marketing Business Acumen What You Will Do - Design, build, and maintain cloud-native data pipelines across AWS Lambda, S3, ECS/Fargate, and SQS - Manage and evolve infrastructure-as-code using Terraform across multiple environments - Build and optimize vector search workflows in Pinecone — including multi-tenant namespace design, metadata schemas, and hybrid search - Develop and improve MCP server integrations using FastMCP SDK and Streamable HTTP transport - Implement and harden authentication flows using AWS Cognito and Secrets Manager - Monitor pipeline health via CloudWatch — log groups, alarms, and observability for Lambda and ECS - Apply cost modeling discipline — flag and mitigate high-cost operations (e.g. embedding calls at scale) - Work within an established SA governance model, contributing to architectural decisions with clear documentation and standards - Collaborate with engineering and applied science teams to deliver reliable, scalable data infrastructure Why This Could Be Your Next Big Move - 🧱 Own the data foundation — You're not maintaining legacy systems. You're designing the infrastructure that AI products are built on top of. - 🤖 Work at the AI/data intersection — Vector search, embeddings, MCP integrations, and multi-tenant architectures. This is where data engineering and AI meet. - ☁️ Deep AWS stack — Lambda, ECS, Cognito, SQS, Secrets Manager and more — a real opportunity to broaden and deepen your cloud expertise across a full production stack. - 🔝 High ownership, high impact — Fast-moving environment where your architectural decisions shape the product directly, with visibility across the entire engineering org. Benefits & Compensation - 💵 $[3,500] – $[5,000]/month — paid in USD, bi-weekly via Deel - 🌎 Fully Remote — work from anywhere in Latin America - 📄 Full-time contract with a U.S. company — 6-month initial term, full-time conversion - 🏖️ Paid PTO — competitive package, grows with tenure - 🤝 Referral Program — earn a bonus for referring talent that gets hired To Apply Please send your resume in English. Include the following depending on your role: - 🔗 LinkedIn Profile URL (required) - 💻 GitHub Portfolio or code samples — show us your pipeline and infrastructure work (required) - ✉️ Cover Letter — tell us about a data pipeline or cloud architecture you built and the impact it had (optional but encouraged) About OneSeven Tech Founded by James Sullivan, OneSeven Tech is a premier digital product agency serving startups and enterprises. Our clients have collectively raised over $100M in VC, and our enterprise partners include 2,000+ person hospitality groups and NASDAQ-listed companies. We work fully remote, move fast with 1-week sprints, and focus on elegant solutions to complex problems. Salary: 3500 - 5000 USD Per month