How to Become a AI ETL Automation Engineer

Build an end-to-end pipeline that ingests PDF invoices from a folder, uses OpenAI's API to extract structured fields (vendor, date, line items, total), validates the output with Pydantic, and loads the results into a SQLite or PostgreSQL database. Include basic error handling and logging.

Design an Apache Airflow DAG that extracts data from a REST API and a CSV file, uses an LLM to classify and enrich records (e.g., sentiment analysis on text fields, entity extraction), deduplicates records using fuzzy matching, and loads clean data into a data warehouse (BigQuery or Snowflake). Include retries, alerting, and data quality checks.

Build a system that takes product listings from multiple supplier feeds, generates text embeddings using OpenAI or Hugging Face, stores them in Pinecone or ChromaDB, and identifies duplicate or near-duplicate products using cosine similarity. Create a pipeline that merges duplicates and maintains a clean master catalog.

Create a pipeline that processes documents in 5+ languages (contracts, invoices, letters), detects language automatically, routes to appropriate extraction prompts, uses LangChain for multi-step extraction with validation chains, handles OCR for scanned documents, and loads results with full lineage tracking into a warehouse. Include a Streamlit dashboard showing extraction accuracy metrics.

Build a complete system with a Retool or Streamlit frontend where AI-extracted records below a confidence threshold are queued for human review. Reviewers can correct fields, and corrections are stored as few-shot examples that are automatically injected into future prompts. Include analytics on human correction rates, model accuracy over time, and cost per reviewed record.

Architect a streaming pipeline using Apache Kafka or AWS Kinesis that ingests social media posts in real-time, enriches them with LLM-based sentiment analysis, entity extraction, and topic classification, handles late-arriving data and out-of-order events, and loads enriched records into a real-time analytics database. Include cost monitoring and backpressure handling.