SQL, grain, BI
Data Analyst
SQL you can defend, a metric someone will argue with, and a BI-shaped interview loop.
New here
Start here — pick a role, then five steps
Dinesh Chandra writes from production Snowflake, Databricks, and Airflow work. The same warehouse shows up in analyst, engineer, scientist, and ML interviews. Pick your role on DataLane first. Then follow the five steps.
Dashboards, grain, warehouse
BI Analyst
Warehouse SQL at dashboard grain. Windows, ranking, and a refresh that does not lie.
dbt, tests, metrics
Analytics Engineer
Mart SQL, tests that page someone, and a metric finance can trust.
Pipelines, warehouses, on-call
Data Engineer
SQL, orchestration, streaming, and the platform sketch they ask at 3pm.
SQL, stats, experiments
Data Scientist
SQL for features, stats without vibes, and a holdout you would defend.
Train, serve, rollback
ML Engineer
Features as tables. Latency as an SLA. Metrics you can put in CI.
Gates, serving, incidents
MLOps Engineer
The SQL that proves training and serving agree, then the gate that blocks a bad model.
RAG, eval, retrieval
AI Engineer
Retrieval is a pipeline. Eval is a job. The model brand is not the incident.
Grain, cost, paved roads
Data Architect
You still have to query the mart you designed. Quality SQL and a time you said no.
Step-by-Step Learning Path
Your 5-Step Blueprint to Data Mastery
Follow this proven sequence from foundation to interview offer.
🧭Step 1
Phase 1 of 5Recommended
Pick the role you want next
Analyst, BI, analytics engineer, data engineer, scientist, ML, MLOps, AI engineer, or architect. Open that flowchart, then the coding pads and interview track for the same role.
💾Step 2
Phase 2 of 5Recommended
SQL you can defend
Windows, grain, and anti-joins. Analysts, engineers, and scientists all sit this pad. Filter the problem list by your role, then read the window guide.
🐍Step 3
Phase 3 of 5Recommended
Python when the screen asks for it
NumPy, plain Python, or PyTorch on the same problem. Scientists and ML engineers live here; analysts and DEs still get asked means, joins-in-code, and metrics.
⚡Step 4
Phase 4 of 5Recommended
One warehouse, cost first
Snowflake credits and Query Profile before Cortex. If you live on Databricks, start with Delta instead. BI roles: still learn who pays for the extract.
🎯Step 5
Phase 5 of 5Recommended
Interview and certs
Open the track for your role, run the coding labs, then sit a practice exam if your loop uses one.