RAMESH MOKARIYA
CERTIFIED DATA ENGINEER
Turning raw data into reliable, scalable business value.
CONTACT ME.
Turning raw data into reliable, scalable business value.
CONTACT ME.
I started with statistics, not software.
Curiosity led me through eight months of intense self-learning
and into the world of data engineering.
Now, I create more than pipelines. I build reliable data
foundations shaped by persistence, logic, and a deep understanding of how data truly works.
Learning and Practicing Data
Analysis and Machine Learning.
Specialized in Statistical Analysis and Machine Learning.
Contributed to building a centralised data warehouse for a large healthcare provider, helping consolidate patient records, claims, and operational data from multiple source systems. Was involved in setting up Airflow-based ingestion pipelines and supporting the Snowflake environment used by reporting teams.
Part of a team delivering data pipeline work for a major theme park group, focused on ticketing and guest transaction data. Worked on dbt models and Airflow DAGs that fed cleaned data into Snowflake, supporting park-level analytics consumed by operations and planning teams.
Contributed to a data engineering team ingesting real-time flight schedules, delay events, and ground operations data from multiple airline feeds. Involved in building Spark and Azure Databricks jobs to process and standardise data, with outputs consumed by operations teams for daily planning.
A beginner-friendly breakdown of how raw data travels from source systems all the way to dashboards - ingestion, transformation, storage, and serving explained in plain terms.
READ MOREWhat makes Snowflake different? Explore its unique multi-cluster shared-data architecture, virtual warehouses, and how it separates storage from compute - all from first principles.
READ MOREUnderstanding Apache Spark from the ground up - Drivers, Executors, RDDs, and DAGs explained simply so you know exactly what happens when your Spark job runs.
READ MOREDrop a message and I'll respond within 48 hours.