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AI Data Engineer
Gomes de Matos Consultores Associados. Participate in requirements-gathering sessions with clients and stakeholders.
Tech Stack
Tools & technologiesAirflowApacheAWSAzureCloudDockerETLGoogle Cloud PlatformGrafanaMongoDBMySQLNoSQLPostgresPrometheusPythonRedisSQL
About the role
Key responsibilities & impact- Participate in requirements-gathering sessions with clients and stakeholders.
- Develop and maintain data ingestion, transformation, and processing pipelines (ETL/ELT).
- Integrate data from APIs, databases, spreadsheets, and other systems.
- Design, implement, and optimize relational, analytical, and NoSQL databases.
- Develop Artificial Intelligence solutions using LLMs, RAG, and AI agents.
- Develop APIs for integration between systems and applications.
- Support the deployment and monitoring of solutions in cloud environments.
- Document processes, pipelines, and developed solutions.
- Collaborate with the team on the continuous improvement of Data and AI solutions.
Requirements
What you’ll need- Apache Airflow; dbt; Airbyte; ClickHouse; MongoDB; Redis; FastAPI; LangChain; LangGraph; LlamaIndex
- Vector databases (Qdrant, pgvector, Pinecone, Weaviate, or similar)
- Hybrid search and embeddings
- CI/CD
- Observability (Grafana, Prometheus, or similar tools)
- Data warehouses and data lakes
- Python; SQL; relational database modeling
- PostgreSQL, MySQL, SQL Server, or equivalent databases
- ETL/ELT concepts
- Data integration across different systems
- Git; Docker; REST APIs
- Basic knowledge of cloud platforms (AWS, Azure, or GCP)
- Knowledge of or experience with Generative AI (OpenAI, Claude, Gemini, or similar tools)
- Prompt engineering
- RAG (Retrieval-Augmented Generation) concepts