Chandrasekhar Muppana
Senior Data Engineer • Hyderabad • m************@gmail.com • 709****636 • linkedin/••••• • portfolio/•••••
Professional Summary
Senior Data Engineer with 4.4 years designing and delivering scalable Azure-based data platforms. Expert in Azure Data Factory, Azure Synapse Analytics, ADLS Gen2 and Power BI; strong background in ETL/ELT architecture, dimensional modeling and query performance optimization. Proven record: architected production ETL pipelines processing 10M+ records/day, improved query and dashboard performance by up to 45%, and implemented data quality and SLA-driven monitoring to increase reliability across insurance and CRM analytics platforms.
Technical Skills
Programming Languages: Python
Databases: SQL,SQL Server,Cosmos DB,Azure Synapse SQL pools,CSV,Flat files,Excel data sources
Cloud and DevOps: Microsoft Azure,Azure Data Factory,Azure Synapse Analytics,Azure Data Lake Storage Gen2,Azure Blob Storage,Azure Key Vault,Azure Monitor,Visual Studio
Data and Analytics: ETL,ELT architecture,Dimensional modeling,Star schema design,Data warehousing,Incremental loads,Data quality & validation,SLA management,Power BI
Tools and Methodologies: Git,Jira,Scrum,Agile
Skills: T-SQL,DAX,Cosmos DB scripting
BI & Reporting: SSRS,Power Query,Row-Level Security,Report performance optimization
Work Experience
LTIMindtree
Hyderabad
Senior Data Engineer
Jan 2022 – Present
Tech Stack: Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Blob Storage, SQL Server, T-SQL, Power BI, SSRS, DAX, Azure Monitor, Azure Key Vault, Cosmos DB, Git, Jira, Python, Visual Studio
- Architected and delivered end-to-end reporting and ETL solutions for the ANIC 2.0 insurance reporting platform, building SSRS parameterized/drill-through reports with role-based security to support enterprise stakeholders; implementation recognized with client Appreciation Certification for on-time delivery.
- Designed and optimized dimensional star-schema models and Synapse-based semantic layers to support high-volume insurance BI; improvements to schema design and indexing reduced query execution time by up to 40% for key reports.
- Built and operated production-grade ADF batch ETL pipelines processing 10M+ records/day with automated monitoring, retry logic, SLA orchestration and alerting, increasing pipeline uptime and reducing failure rates by ~35%.
- Implemented a data quality framework (row-count reconciliation, null/schema checks, record-level validation) and reconciliation processes across ETL flows, reducing data discrepancy incidents by 30% and improving trust in downstream analytics.
- Led end-to-end Power BI development for CRM and marketing analytics—advanced DAX measures, aggregations, query folding and RLS—and integrated Azure Synapse to streamline reporting; achieved ~45% dashboard performance gains and cut report refresh/turnaround time by ~25%.
- Optimized complex SQL workloads through indexing, partitioning, execution plan analysis and query refactoring; automated ingestion using ADF Copy Activity, Get Metadata and control flows to reduce operational overhead by 20% and accelerate campaign/customer-segmentation analytics.
Marketing Analytics
Integrated Azure Synapse Analytics
Education
GMR Institute of Technology
Bachelor of Civil Engineering • India • July 2017 – Sept 2020
Certifications
Microsoft Certified: Power BI Data Analyst (PL-300) — Microsoft • 2026
SQL for Data Analysis — Simplilearn Skill up • 2026
Oracle SQL and Relational Databases (SQL 101) — Oracle / course provider
Achievements
- Appreciation Certification — Copper Point Insurance: Recognized by client leadership for on-time delivery and technical excellence on the ANIC 2.0 enterprise reporting platform.
- Query and Dashboard Performance Improvement: Drove 40–45% improvement in SQL query and Power BI dashboard performance across insurance and CRM reporting platforms through indexing, partitioning, DAX optimization and aggregation strategies.
- Enterprise ETL Delivery: Delivered 10+ production ETL pipelines integrating SQL Server, Azure Synapse, Excel and flat files—improving data availability by ~35% and eliminating manual processing.
- Data Quality Framework Implementation: Implemented end-to-end data quality and reconciliation processes, reducing data discrepancy incidents by ~30%.
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