Harish Kumar
Data engineering
Building data pipelines, ingestion workflows, streaming systems, and reliable data processing.
Harish Kumar
/ Data engineer
Building data systems
that turn raw events
into business insights.
I build scalable data pipelines, cloud data platforms, analytics workflows, and business intelligence solutions using the modern data stack.
- Snowflake
- SQL
- Snowpipe
- AWS
- dbt
- Kafka / Redpanda
- Power BI
- PythonEvent generation
- Redpanda / KafkaStreaming
- AWS S3Cloud storage
- SnowflakeData warehouse
- dbtTransformation
- Power BIAnalytics
All systems operational
About me
I don't just analyze data. I build the systems that make data usable.
I'm Harish Kumar, a data professional focused on building reliable data pipelines, cloud data platforms, data warehouses, transformations, and analytics solutions.
I work across the modern data stack, from generation and ingestion to storage, transformation, modeling, visualization, and business intelligence.
Work togetherCloud data platforms
AWS S3, Snowflake, cloud storage, data warehousing, and scalable architectures.
Analytics and BI
SQL, dbt, Power BI, dimensional modeling, business KPIs, and dashboards.
The stack
A modern stack, used with intent.
The tools matter. The system thinking matters more.
Data engineering
- SQL
- Kafka
- Redpanda
- ETL
- ELT
- Data pipelines
Warehousing
- Snowflake
- Data warehouse design
- Dimensional modeling
- Star schema
Cloud
- AWS
- AWS S3
- Cloud storage
Transformation
- dbt
- SQL models
- Data quality tests
Analytics
- Power BI
- Business KPIs
- Dashboards
Development
- Python
- Git
- GitHub
Selected work
Projects that move from raw to ready.
Real data systems for real business problems.
Real-time retail data platform
An end-to-end retail platform that moves historical and streaming events into trusted, business-ready models.
- Python
- Redpanda
- AWS S3
- Snowflake
- dbt
- Power BI
Open Power BI report
Hotel booking data warehouse
A cloud warehouse that turns raw booking records into clean analytical models and reliable reporting layers.
- Snowflake
- SQL
- dbt
- Python
- Power BI
Open Power BI report
Customer and sales analytics
An analytical solution for understanding sales, customer behavior, product performance, returns, and KPIs.
- SQL
- Snowflake
- Power BI
- DAX
Open Power BI report
Flagship case study
A complete data platform for a moving business.
Historical and streaming retail events, shaped into a reliable system for inventory, sales, customers, stores, and returns.
Retail teams need visibility now.
Sales, product availability, inventory, returns, customers, and stores all tell a different part of the story. The system connects them.
One path from event to insight.
Python generates events, Redpanda streams them, S3 preserves the raw layer, Snowflake warehouses it, dbt models it, and Power BI makes it useful.
The path of a retail event
Select a step to inspect it.
A star schema for clear questions.
A simple analytical shape keeps business concepts easy to query, test, and explain.
FACT_RETAIL_EVENTS
DIM_CUSTOMER
DIM_PRODUCT
DIM_STORE
DIM_DATE
Data flow
- Raw event
- Validation
- S3 landing
- Snowflake staging
- dbt transformation
- Fact and dimensions
- Power BI
How I build
Good data work compounds.
The best systems make the next question easier to answer than the last.
Reliable
Data pipelines should be predictable and observable.
Scalable
Design systems that can grow with data volume.
Tested
Data quality should be validated before analytics.
Modeled
Transform raw data into clear analytical models.
Automated
Reduce repetitive manual processes.
Business-driven
Technology should solve real business problems.
Pipeline status
Quietly reliable by design.
The system is only impressive when it keeps doing its job.
- IngestionOnline
- StreamingOnline
- S3 storageOnline
- SnowflakeOnline
- dbtOnline
- Power BIOnline
- System statusAll systems operational
Code that builds the system
Show the work. Share the thinking.
retail-data-platform
An end-to-end retail platform that moves historical and streaming events into trusted, business-ready models.
Python, Redpanda, AWS S3hotel-booking-warehouse
A cloud warehouse that turns raw booking records into clean analytical models and reliable reporting layers.
Snowflake, SQL, dbtcustomer-sales-analytics
An analytical solution for understanding sales, customer behavior, product performance, returns, and KPIs.
SQL, Snowflake, Power BIThe next step
Engineering depth. Business context.
Interested in building reliable data systems, cloud data platforms, and analytics solutions that teams can trust.
- Snowflake Data engineer
- Snowflake dbt developer
- Snowflake ETL/ELT engineer
Open to data engineering opportunities
Let's build something with data.
Open to opportunities, collaborations, and interesting data engineering problems.
Email and WhatsApp are open for direct conversations.