01ARCHITECTURE
Lakehouse pipelines
Build layered data processing workflows with Delta Lake and the Bronze, Silver, and Gold approach to organize raw, validated, and analytics-ready data.
DATA ENGINEER · OPEN TO OPPORTUNITIES
Turning raw data into reliable data products.
I’m Rajesh, a Data Engineer building and maintaining data pipelines with PySpark, Azure Databricks, Delta Lake, and cloud storage. I focus on dependable processing, data quality, and analytics-ready datasets.
~1Mraw records processed daily
~1 GBdaily raw data volume
3 layersBronze · Silver · Gold
retail_lakehouse.pyPYSPARK
01from pyspark.sql import SparkSession
02
03raw_data = read_source("retail")
04validated = validate_schema(raw_data)
05clean_data = transform(validated)
06write_delta(clean_data, "curated")
07# Quality-checked, analytics-ready data
BronzeIngest
→
SilverClean
→
GoldCurate
ETL / ELT PIPELINEQUALITY · RELIABILITY · SCALE
01 / About
Engineering data you can trust.
My work sits at the intersection of data transformation, lakehouse design, quality checks, and workflow reliability.
02DATA QUALITY
Validation by design
Check schemas, nulls, duplicates, missing records, and source-to-target consistency to help keep downstream datasets dependable.
03OPERATIONS
Reliable delivery
Orchestrate batch processing, monitor workflow runs, investigate failures, manage schema changes, and support CI/CD practices.
02 / Selected work
Practical data engineering.
Representative work based on my professional experience. Client environments and proprietary implementation details are not reproduced here.
↳RETAIL DATA
E-commerce lakehouse pipeline
Developed PySpark ETL for T-1 retail data processing, moving raw data into Bronze, Silver, and Gold layers and preparing sales, product, and geographic datasets for analytics.
✓DATA QUALITY
Validation & reconciliation
Validated source datasets, cleansed and transformed data with PySpark and SQL, and checked source-to-target outputs to identify missing records and inconsistencies.
⌘ORCHESTRATION
Batch workflows & delivery
Configured Azure Data Factory and Databricks workflows for batch integration, supported job monitoring and failure investigation, and worked with Azure DevOps and Git.
Note: These are experience-based case summaries, not claims of separately published or open-source projects.
03 / Career
Experience that ships.
Data engineering and software development across retail, e-commerce, and enterprise applications.
Data Engineer
DEC 2025 — PRESENT2GBR Software Pvt Ltd · Best Buy e-commerce & retail project · Pune, India
- Designed and developed end-to-end PySpark ETL pipelines on Azure Databricks for T-1 processing, handling approximately 1 million raw records (~1 GB) daily.
- Built and maintained Bronze, Silver, and Gold data layers using Delta Lake on AWS S3.
- Developed transformations for sales datasets, including cleansing, standardization, joins, and aggregations.
- Performed data quality and schema validation; supported partitioning and Spark configuration optimization.
- Orchestrated jobs, investigated failed runs, managed schema evolution and alerts, and contributed reusable Python modules and CI/CD workflows.
Associate Data Engineer
OCT 2023 — NOV 2025Synergie Technik Pvt Ltd · Kohl’s e-commerce project · Pune, India
- Validated source datasets for nulls, duplicates, missing records, and inconsistencies.
- Cleansed and preprocessed data using PySpark and SQL.
- Performed source-to-target validation and reconciliation to verify ETL outputs.
- Investigated data-quality issues and pipeline failures, supporting root-cause analysis and resolution.
- Created Azure Data Factory pipelines for batch orchestration and integration with Azure Databricks.
Software Engineer
APR 2023 — OCT 2023Synergie Technik Pvt Ltd · Manufacturing ERP system
- Developed responsive front-end pages for inventory, machining operations, production planning, quality checks, invoicing, and dispatch handoff.
- Worked with backend teammates to integrate interfaces and resolve UI issues during testing.
04 / Toolkit
Tools of the trade.
A practical stack for building, validating, and operating data pipelines.
Big data & lakehouse
Cloud & data platforms
Programming & databases
Engineering & collaboration
05 / Education
Learning foundation.
PG-Diploma in Advanced Computing (PG-DAC)
CDAC · Thiruvananthapuram, Kerala
Bachelor of Engineering (B.E.)
Sinhgad Institute of Technology · SPPU, Maharashtra
06 / Contact
Let’s build something dependable.
Interested in a data engineering opportunity or want to discuss data pipelines? Send me a message using the form and I’ll get back to you.
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