Nithyananda Bhujangarao

Angestellt, PRINCIPAL ENGINEERING MANGER, Circana LLC
Bangalore, Indien

Fähigkeiten und Kenntnisse

Talend
ETL (Extract Transform Load)
Data Warehouse
Data Modeling
Hadoop Distributed File System
Apache Hadoop
Apache Spark
Software Development
Azure Databricks
Azure Data Factory
Azyre Synapse
Scrum
Jira

Werdegang

Berufserfahrung von Nithyananda Bhujangarao

  • Bis heute 8 Monate, seit Mai 2025

    PRINCIPAL ENGINEERING MANGER

    Circana LLC

    Principal Engineering Manager with 13+ years of experience delivering Azure-native data engineering platforms for analytics and data warehousing at enterprise scale. Strong background in designing ETL/ELT pipelines, optimizing cloud performance and cost efficiency, and building reliable data products using Azure services. Combines deep technical expertise with engineering leadership to drive predictable delivery and client-facing outcomes.

  • 8 Monate, Juli 2019 - Feb. 2020

    Technical Lead

    Alten Calsoft Labs India Pvt Ltd

    • Translated complex business requirements into robust dimensional models (star and snowflake schemas) and designed Enterprise Data Warehouse (EDW) structures to support scalable analytics. • Engineered data ingestion from Graph DBs and RDBMS into Hive-based data lakes and EDW layers. • Supported migration of Talend ETL jobs to Big Data platforms, strengthening large-scale data processing capabilities.

  • 8 Monate, Dez. 2018 - Juli 2019

    Senior Software Engineer

    Intellifour Software

    • Built Talend Big Data pipelines to generate KPI-driven Target Scores, enabling identification of ~60% high-value target customers. • Developed automated audit and email notification frameworks for 38 ETL workflows, reducing manual monitoring effort by 25%.

  • 6 Jahre und 4 Monate, Sep. 2012 - Dez. 2018

    Technical Lead

    HARMAN Connected Services

    • Converted client requirements into enterprise-grade data models for analytics platforms. • Automated AS OLAP model creation by building C# automation that orchestrated Analytical Server (AS) engine APIs for dimension/fact modeling, reducing manual modeling effort by 90%. • Owned end-to-end training and onboarding of new engineers on proprietary analytics platforms, enabling faster integration into active projects and consistent delivery standards.

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