- Vishakha Sadhwani
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- Cloud Data Engineer Learning Path
Cloud Data Engineer Learning Path
A clear role breakdown, skill map, and certification path.
Vishakha Sadhwani
November 13, 2025
Hi Inner Circle,
Welcome back to the series ~ where we talk about the real roles shaping the future of Cloud and AI Infrastructure.
If Cloud Engineers build the foundation and ML Engineers build the intelligence, Data Engineers build the pipelines that bring everything to life.
This is the role that carries data from source → storage → transformation → analytics → AI.
So, what does a Data Engineer really do?
In the simplest terms, Data Engineers create the systems that collect, clean, organize, and deliver data across an organization.
They don’t run ML experiments.
They don’t build dashboards.
Their job is to make sure the right data shows up in the right shape at the right time.
Think of it like this:
~ Data Analysts interpret data.
~ Data Scientists model data.
~ Data Engineers make the data usable.
……
In real life, Data Engineers:
→ Build ingestion pipelines from APIs, apps, logs, and databases
→ Manage data lakes and warehouses
→ Automate batch + streaming transformations
→ Optimize query performance and reduce storage/compute costs
→ Enable ML and BI teams with structured, reliable datasets
You become the backbone of the company’s analytics and AI ecosystem.
Where does Data Engineering fit into the AI wave?
AI systems depend on high-quality, real-time, and context-rich data.
With the rise of LLMs, RAG systems, and vector search, Data Engineering now involves:
- Vector databases for embedding search
- Lakehouse architectures for unified data
- Real-time pipelines for feature delivery
- Stream processors for low-latency inference
SO AI didn’t replace Data Engineering .. it expanded it.
Cloud Data Engineering - The 5-Level Path
(Each level: what you do → cloud services you learn)
Basic foundations:
→ Programming: Python for ETL + automation, SQL for querying and modeling.
→ Data Systems: Relational (PostgreSQL/MySQL) + NoSQL basics (MongoDB, DynamoDB).
→ Processing Concepts: Batch vs streaming, Spark fundamentals, Kafka fundamentals.
→ Cloud & Storage Basics: S3 / ADLS / GCS, object storage concepts, VPC/network basics.
→ Orchestration & Automation: Airflow/Luigi, Git, Docker, CI/CD basics.
→ Data Modeling: Normalization, denormalization, OLTP vs OLAP, partitioning principles.
Level 1 — Ingest & Land Raw Data
Bring data from applications, databases, logs, and events into the cloud in its raw form.
→ Batch ingestion:
AWS (DMS) • Azure (Data Factory) • GCP (Datastream) • OCI (GoldenGate)
→ Streaming ingestion:
AWS (Kinesis) • Azure (Event Hubs) • GCP (Pub/Sub) • OCI (GoldenGate Streams)
→ Raw storage layer:
AWS (S3) • Azure (ADLS Gen2) • GCP (Cloud Storage) • OCI (Object Storage)
→ Schema + metadata:
AWS (Glue Data Catalog) • Azure (Purview) • GCP (Data Catalog) • OCI (Data Catalog)
→ Access & security:
AWS (IAM) • Azure (RBAC) • GCP (IAM) • OCI (IAM + Vault)
Level 2 — Clean, Transform & Structure Data
Make raw data usable through transformations, enrichment, and modeling.
→ Batch transformations (Spark/ETL):
AWS (Glue ETL / EMR Spark) • Azure (Databricks / Synapse Spark) • GCP (Dataflow / Dataproc) • OCI (Data Flow)
→ Event-driven enrichment:
AWS (Lambda) • Azure (Functions) • GCP (Cloud Functions) • OCI (Functions)
→ SQL on data lakes:
AWS (Athena) • Azure (Synapse Serverless SQL) • GCP (BigQuery external tables) • OCI (ADW external)
→ Analytical warehouse:
AWS (Redshift) • Azure (Synapse Dedicated SQL) • GCP (BigQuery) • OCI (Autonomous Data Warehouse)
Level 3 — Build the Data Platform (Lakehouse Layer)
Design the scalable structure behind your pipelines — open table formats, orchestration, and storage patterns.
→ Lakehouse table formats:
AWS (Iceberg on S3) • Azure (Delta Lake on ADLS) • GCP (BigLake tables) • OCI (Parquet + ADW)
→ Pipeline orchestration:
AWS (Glue Workflows / Step Functions) • Azure (ADF / Synapse Pipelines) • GCP (Cloud Composer) • OCI (Data Integration Flows)\
→ Optimization & layout:
AWS (Parquet + S3 partitioning) • Azure (Delta Z-order) • GCP (BQ partitioning + clustering) • OCI (Partitioned Parquet)
→ Lifecycle management:
AWS (S3 Lifecycle Rules) • Azure (ADLS Tiering) • GCP (Storage Classes) • OCI (Lifecycle Policies)
Level 4 — Scale, Observe & Govern
Make pipelines enterprise-grade: more reliability, less cost, tighter security.
→ High-scale compute:
AWS (EMR) • Azure (Synapse Spark) • GCP (Dataflow Autoscaling) • OCI (Data Flow autoscaling)
→ Data quality & validation:
AWS (Deequ / Glue Data Quality) • Azure (Purview Quality) • GCP (Dataplex Quality) • OCI (Data Integration Checks)
→ Monitoring & observability:
AWS (CloudWatch) • Azure (Monitor + Log Analytics) • GCP (Cloud Monitoring) • OCI (OCI Monitoring)
→ Governance & permissions:
AWS (Lake Formation) • Azure (Purview) • GCP (VPC Service Controls + IAM) • OCI (IAM Policies + Vault)
→ Cost controls:
AWS (S3 tiers, Redshift RA3) • Azure (Reserved capacity, tiering) • GCP (BQ slot management) • OCI (Autoscaling + lifecycle rules)
Level 5 — Deliver Data Products & Pipelines
Expose high-quality data to ML teams, BI tools, and real-time applications.
→ Analytics datasets:
AWS (Redshift / Athena) • Azure (Synapse SQL) • GCP (BigQuery) • OCI (ADW)
→ BI dashboards:
AWS (QuickSight) • Azure (Power BI) • GCP (Looker) • OCI (Oracle Analytics Cloud)
→ ML pipelines / Feature stores:
AWS (SageMaker Feature Store) • Azure (Azure ML) • GCP (Vertex AI + BQ ML) • OCI (Oracle Data Science)
→ Low-latency serving:
AWS (Kinesis Analytics) • Azure (Stream Analytics) • GCP (Bigtable / Firestore) • OCI (GoldenGate Real-Time)
Certification Guide ~ Multi-Cloud Data Engineer Path
AWS
→ AWS Cloud Practitioner (optional)
→ AWS Solutions Architect Associate
→ AWS Machine Learning Specialty (optional)
Azure
→ AZ-900 Azure Fundamentals
→ AZ-104 or AZ-305 (optional platform track)
GCP
→ Associate Cloud Engineer
→ Professional Cloud Architect (optional)
OCI
→ OCI Foundations Associate
→ Autonomous Database Specialist (optional)
Projects You Can Build:
Your Takeaway
Data Engineers don’t just move data.. they build the systems that power AI, analytics, and intelligent applications.
Learn the fundamentals. Build meaningful projects.
You got this!
– V
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