Truelogic The Lead Data Engineer at Truelogic will architect and build scalable data infrastructure for real-time AI applications. This role involves transforming static data stores into active backends and establishing engineering best practices.
Responsibilities
Architect & Activate Data: Design, build, and scale high-throughput, low-latency data infrastructure connecting cloud data lakes/warehouses to dynamic AI backends and real-time agentic workflows.
Transform Latent Stores: Operationalize static data repositories into active context layers, vector engines, and memory systems for autonomous AI agents and downstream applications.
Real-Time & Event-Driven Pipelines: Build real-time ingestion, event-driven streaming, and retrieval pipelines (RAG, vector stores, operational databases) to feed live AI applications.
Technical Leadership & Standards: Establish data architecture standards, CI/CD, documentation, and data quality best practices while mentoring data engineers.
Cross-Functional Collaboration: Partner with Machine Learning Engineers, Product teams, Creative Directors, and agency stakeholders to translate client and business requirements into production-grade data APIs and schemas.
AI-Enabled Engineering: Leverage AI-powered coding, agentic engineering, and modern automated workflows to accelerate development and execution.
Qualification
5+ years in Data EngineeringBeyond Data MigrationsAgentic & Real-Time AI BackendsDeep Cloud ExpertiseCore Technical StackAI Workflow MasteryLeadership & CommunicationLocated in Latin AmericaFluent in English
Required
5+ years in Data Engineering: Demonstrated experience designing, building, and scaling data pipelines and cloud infrastructure from the ground up.
Beyond Data Migrations: Proven track record of activating data infrastructure for dynamic, real-time downstream applications rather than focusing solely on batch ETL/ELT or static warehousing.
Agentic & Real-Time AI Backends: Direct experience building data layers for real-time, event-driven applications, vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector), or retrieval-augmented generation (RAG) at scale.
Deep Cloud Expertise: Strong experience with major cloud platforms (AWS, GCP, Azure) and modern cloud data stacks (Snowflake, Databricks, BigQuery). Bring your opinions on what works and what doesn't!
Core Technical Stack: Advanced proficiency in Python and SQL for data processing, real-time pipeline construction, and close collaboration with ML teams.
AI Workflow Mastery: Experience utilizing AI-assisted development tools, agentic coding, and automated engineering workflows.
Leadership & Communication: Track record of mentoring engineers and setting engineering standards. Exceptional communication skills suited for a hybrid Tech/Creative culture.
