🕒 5 days ago
Build Evervault’s data platform from the ground up, owning real-time ingestion, time-series foundations, customer-facing data products and APIs, and production reliability while setting the architecture and technical direction.
Senior FDE (Forward Deployed Engineer) - Databricks/Spark, Brazil
🕒 yesterday
Apache SparkDatabricksPysparkDbt
📜 Description
- Build distributed, scalable Apache Spark pipelines to ingest, process, and transform large volumes of data from client systems.
- Turn raw data into structured datasets tailored to business use cases and AI agents.
- Work directly with clients to translate business needs into reliable data flows.
- Deliver end-to-end applications and integrate them with existing client infrastructure.
- Deploy solutions in Azure and apply testing, code review, CI/CD, and security practices throughout delivery.
🛠️ Requirements
- 7–10+ years of practical experience delivering full-stack applications.
- Deep proficiency in Python and TypeScript/JavaScript.
- Strong experience with RESTful and GraphQL APIs, SQL and NoSQL databases.
- Hands-on experience with LLM APIs and practical understanding of prompting, RAG-style Q&A, streaming UX, and common failure modes.
- Daily use of AI-assisted development tools such as Cursor, GitHub Copilot, or Claude Code, with disciplined review, testing, and refactoring.
- Experience with Azure, Databricks, dbt, and PySpark.
- Professional written and spoken English for technical collaboration.
- Clear communication about task status, requirements, risks, and blockers.
✨ Benefits
- Health and dental insurance
- Meal and food allowance
- Childcare assistance
- Extended paternity leave
- Partnership with gyms and health and wellness professionals via Wellhub (Gympass) TotalPass;
- Profit Sharing and Results Participation (PLR);
- Life insurance
- Continuous learning platform (CI&T University);
Full job description
At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
We’re looking for a Spark FDE to build the data foundation behind our AI agents. You’ll need hands-on experience with Apache Spark, including ingesting, processing and transforming large volumes of data from different client systems. We expect strong skills in designing distributed, scalable pipelines and in turning raw data into structured datasets for each business case. You should be comfortable working directly with clients and turning their business needs into reliable data flows. Above all, you’ll be a full-stack engineer who understands both big data and the business, and who can deliver production-ready pipelines that power working agents.
Requirements
7–10+ years of practical experience in delivering fullstack applications
1. Software Engineering Fundamentals and Integration
Build robust end-to-end solutions and connect them to existing infrastructures.
Programming Languages: Deep proficiency in Python (the gold standard for AI and data) and TypeScript/JavaScript (essential for web applications and APIs).
API Development and Consumption: Absolute mastery of RESTful and GraphQL architectures to integrate the core product into the client's legacy systems.
Databases: Solid knowledge of both traditional relational databases (SQL) and non-relational databases (NoSQL) to extract data from wherever it lives.
2. Artificial Intelligence and Data Ecosystems (The core differentiator)
AI-assisted development: daily use of Cursor, GitHub Copilot, or Claude Code; disciplined about reviewing, testing, and refactoring agent-generated code to team standards.
LLM product basics: hands-on with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or equivalent); practical understanding of prompting, RAG-style Q&A, streaming UX, and common failure modes (hallucination, latency, cost).
3. Infrastructure, Cloud, and DevOps
Deploy code seamlessly into the client's cloud environment (Azure).
Engineering standards: comfort applying tests, code review, CI/CD, and security practices during iterative delivery, not only at the end of a project.
Data Science / Data Engineering: Core tools for handling pipelines and cleaning large data volumes (minimum requirements are Databrics, DBT and PySpark).
4. Communication
Professional English in a technical environment is mandatory (written and spoken) for specifications, reviews, risk communication, and day-to-day collaboration.
Good communication to align task status, understanding of requirements and escalate any hurdles or blockers that impact the work to be done
#LI-BM2
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