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Bikram User

@bikramuser1

Message

Azure data engineer building Lakehouse ETL pipelines for analytics-ready datasets.

India

Message

What I'm looking for

I’m looking to build reliable Azure Lakehouse ETL/ELT pipelines—incremental and batch/real-time where needed—using Databricks, Delta, and dbt, with strong data quality, modeling, and Power BI–ready datasets.

I build ETL/ELT pipelines in Azure using Azure Data Factory, Databricks (PySpark), and Azure Data Lake Storage (ADLS) to ingest SQL Server, MongoDB, and flat-file data into a Lakehouse.

At LTIMindtree, I’ve implemented Medallion Architecture with Delta Lake and dbt—using cleansing, validation, flattening, and incremental processing (Auto Loader and Delta Lake Change Data Feed) so downstream reporting stays fresh.

I also optimize Spark and Delta Lake workloads through partitioning, caching, and parallel processing, and I’ve helped reduce report runtime by improving curated Gold-layer models served to Power BI Service and Power BI Embedded.

When pipelines need to scale, I parallelize concurrent ETL batches, automate validation/testing with Databricks notebooks, and load curated datasets into Azure SQL Database for applications and APIs.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

LTIMindtree

Aug 2024 - Present (1 year 11 months)

Developed ETL/ELT pipelines using Azure Data Factory, Databricks (PySpark), and Azure Data Lake Storage (ADLS) to ingest structured (SQL Server), semi-structured (MongoDB), and flat-file data into a cloud-based Lakehouse, processing 200+ GB of enterprise data.