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

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