Completed Associate Data Engineer in SQL
Joins, database design, data warehousing, and Snowflake SQL — wrapping up a full career track and what it taught me about structuring data for scale.
Shaharier Shourov
Data Analyst & Aspiring Data Engineer
Closing out the SQL track
I just completed DataCamp's Associate Data Engineer in SQL career track — a milestone I've been working toward for a few months alongside my day job at New Era Cap.
"SQL isn't just a query language — it's the foundation every other data engineering tool sits on top of."
What the track covered
- Joins and set operations — inner, outer, self joins, and combining result sets with UNION and INTERSECT
- Database design — normalisation, primary/foreign keys, and when to denormalise for performance
- Data warehousing concepts — star schemas, fact and dimension tables, and OLTP vs OLAP thinking
- Snowflake SQL — cloud warehouse-specific syntax, virtual warehouses, and cost-aware querying
Why this mattered for my transition
Coming from Textile Engineering and Merchandising, I didn't have a formal CS background. This track filled in the structural gaps — not just "how do I write a query" but "how should data actually be organised so queries stay fast and maintainable at scale."
It directly informed how I structured the fact tables and SQL views in my Olist e-commerce analytics project — I wouldn't have known to separate raw tables from analytical views without this foundation.
What's next
With the SQL track complete, I'm circling back to Python — specifically the Data Engineer in Python track — to round out the skill set with API ingestion, scripting-based cleaning, and Airflow orchestration.
