s
srirammurali565

Sriram

@srirammurali565

Data Engineer, Databricks and Fabric Certified, 4 years Experience

Regno Unito
Inglese, Tamil
Alcune informazioni sono riportate in lingua inglese.
Chi sono
Data Engineer with 4 years of experience building enterprise data solutions. Databricks Certified Data Engineer Associate and Microsoft Fabric Certified Data Engineer Associate. I specialize in production-grade data pipelines using Databricks and Fabric with Medallion architecture, Delta Lake, real-time streaming, AI/BI dashboards, and Power BI solutions on Azure. Recent work includes B2B sales pipelines with 26 dbt tests, e-commerce analytics platforms, and real-time order processing. I deliver scalable, well-documented solutions following industry best practices.... Continua a leggere

Competenze

s
srirammurali565
Sriram
offline • 

Consulta i miei servizi

Dashboard dati
I will create a professional power bi dashboard and data model
Dati ETL
I will build databricks data pipeline with delta lake

Portfolio

Esperienza lavorativa

Blues

Data Engineer

Blues

Jan 2024 - Present2 yrs 4 mos

Engineered Python and SQL pipelines for Fabric-style Lakehouse workflows, transforming multi-domain business data into analytics-ready tables and reducing reporting latency by 28 minutes. Designed Medallion-based Lakehouse layers and dimensional models that transformed raw source data into trusted semantic datasets for KPI tracking. Implemented schema validation, reconciliation checks, and exception handling frameworks to ensure data accuracy.

Tech_Cloud

Data Engineer

Tech Cloud

Jan 2022 - Jan 20242 yrs

Designed and optimized Azure Databricks-based distributed data platform, improving storage efficiency by 18% and enabling scalable processing of large datasets. Developed PySpark-based data pipelines using partitioning, caching, and optimization techniques, reducing processing time by 32 minutes for reporting workflows. Built data ingestion and transformation workflows to integrate data across enterprise systems. Implemented pipeline monitoring, logging, and traceability mechanisms to improve failure detection and support auditable production data operations.