Lead Data Engineer - Advertising
New
J
JobgetherAdvertising data
Based in BrazilFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- Fluent in English.
- Experience
- At least 5 years of experience in Data Engineering
- Required Skills
- AWSPythonSQLGCPSnowflakeAzureData engineeringBigQueryDatabricks
Requirements
- Have at least 5 years of experience in Data Engineering.
- Have a proven track record designing and scaling data pipelines and cloud infrastructure from the ground up.
- Have experience building data infrastructure for dynamic, real-time applications beyond traditional batch ETL/ELT and static data warehousing.
- Have hands-on experience developing real-time, event-driven data layers for AI applications.
- Have experience with vector databases such as Pinecone, Qdrant, Milvus, or pgvector, and large-scale Retrieval-Augmented Generation (RAG) systems.
- Have strong expertise in AWS, GCP, or Azure.
- Have experience with Snowflake, Databricks, or BigQuery.
- Be proficient in Python and SQL for data processing and real-time pipeline development.
- Be familiar with AI-assisted development tools, agentic coding, and automated engineering workflows.
- Be able to mentor engineers, establish technical standards, and drive engineering best practices.
- Be based in Latin America.
- Be fluent in English.
Responsibilities
- Architect, build, and scale high-throughput, low-latency data infrastructure connecting cloud data lakes and warehouses to real-time AI applications.
- Transform static data repositories into context layers, vector engines, and memory systems for autonomous AI agents.
- Develop real-time ingestion, event-driven streaming, and retrieval pipelines, including RAG architectures, vector stores, and operational databases.
- Build and optimize data backends for predictive systems, generative AI applications, and agentic workflows.
- Establish data architecture standards, CI/CD practices, documentation, and data quality processes.
- Mentor data engineers and promote engineering excellence and knowledge sharing.
- Collaborate with Machine Learning Engineers, Product teams, Creative Directors, and other stakeholders to deliver production-grade data APIs and schemas.
- Use AI-assisted development tools, agentic coding, and automated engineering workflows.
- Contribute to technical strategy, architectural decisions, and the development of the data engineering organization.
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