While data scientists focus on statistical modeling and exploratory analysis, Data Engineers and MLOps Engineers build the robust plumbing that powers enterprise analytics and production AI. Recruiters in this sector care deeply about throughput, system latency, distributed processing, and operational reliability.

Essential Architecture Keywords for Data & ML Engineers

  • Data Orchestration & Ingestion: Apache Airflow, dbt, Apache Kafka, Spark Streaming, Flink
  • Data Warehousing & Lakes: Snowflake, Google BigQuery, Amazon Redshift, Apache Iceberg, Delta Lake
  • MLOps & Serving: MLflow, Kubeflow, Triton Inference Server, FastAPI, vLLM, TensorRT
  • Data Quality & Governance: Great Expectations, Monte Carlo, data lineage tracking, GDPR/HIPAA compliance

Proving Scale with Concrete Metrics

Always quantify data volumes and execution latency. For example: 'Architected streaming ETL pipeline using Kafka and Spark, processing 45M events daily (3.2 TB/day) into BigQuery with sub-5-second ingestion latency, slashing daily dashboard generation time from 4 hours to 8 minutes.'