To work with today’s modern data engineering services, you need a strong mix of cloud, data, and engineering skills. At a minimum, you should understand:
Core Skills
Cloud platforms (AWS, Azure, or GCP) and their native data services
SQL mastery for analytics, transformations, and performance tuning
Python (or Scala for Spark) for data processing and automation
Modern data stack tools like Snowflake, BigQuery, Databricks, Airflow, dbt, Kafka, and Fivetran
Foundational Knowledge
ETL/ELT, data modeling, batch vs. streaming
Lakehouse and warehouse architectures
Storage formats (Parquet, Avro) and distributed processing concepts
Emerging Skills
Data observability and quality tools
Understanding AI/ML data needs (feature stores, vector databases, embeddings)
Basic DevOps: Git, CI/CD, infrastructure as code
Overall, success in modern data engineering comes from combining solid fundamentals with familiarity across cloud-native tools, automation, and the growing influence of AI.
Top comments (0)