u/Ashamed_Figure7162

  1. Strong SQL & Data Modeling SQL is still the backbone—writing optimized queries and designing efficient data models is essential.
  2. Python for Data Pipelines Python is widely used for building ETL pipelines, automation, and data processing.
  3. Cloud Platforms (AWS / Azure / GCP) Most companies work on cloud—knowing services like data lakes, warehouses, and pipelines is a must.
  4. Big Data Tools Familiarity with tools like Spark, Kafka, and distributed systems helps you handle large-scale data. credo systemz
  5. ETL & Workflow Orchestration Tools like Airflow or Azure Data Factory are key for scheduling, managing, and automating data workflows.
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u/Ashamed_Figure7162 — 17 days ago