Data Engineer (RnD)

we're looking for a Data Engineer (R&D) who's comfortable working in the messy, creative space between raw data and intelligent insight.

You won't just maintain production ETLs — you'll prototype data flows for new AI concepts, test ingestion patterns for unstructured sources, and help MLEs iterate faster by making data explorable, not just available. If you've ever said, "Let me just spin up a quick pipeline to test this idea," you already think the way we do.

This is a hybrid role based in West Jakarta, where engineering meets exploration.

What You'll Do:

✅ Build lightweight, flexible data pipelines to support AI/ML prototyping: RAG, agents, GenAI workflows

✅ Ingest and transform unstructured or semi-structured data (documents, logs, APIs, embeddings) for experimentation

✅ Collaborate with MLEs and product to turn research questions into testable data setups

✅ Implement basic vector indexing, metadata tagging, and retrieval patterns for LLM applications

✅ Document data lineage, assumptions, and limitations — so experiments are reproducible

✅ Support transition of successful prototypes into more robust, production-ready pipelines

✅ Stay curious on emerging data patterns — but filter them through a lens of practicality

Who You Are:

✅ 2–4 years in data engineering, analytics engineering, or applied data roles

✅ Strong in Python, SQL, and comfortable with cloud data platforms (AWS/GCP/Azure)

✅ Experience with Spark, Airflow, or similar orchestration — even in small-scale or experimental contexts

✅ Familiar with LLM-adjacent data work: embeddings, chunking, metadata, vector DBs (Pinecone, pgvector, etc.)

✅ Bonus: Exposure to MLOps basics, Dify/n8n, or rapid prototyping workflows

✅ Fluent in English — written and spoken

✅ Curious, iterative, and passionate about making data useful for discovery, not just reporting