SIVA VISHNU A — AI Researcher

𝗣𝗮𝘀𝘀𝗶𝗼𝗻𝗮𝘁𝗲 𝗮𝗻𝗱 𝗿𝗲𝘀𝘂𝗹𝘁𝘀-𝗱𝗿𝗶𝘃𝗲𝗻 𝗗𝗮𝘁𝗮 & 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘄𝗶𝘁𝗵 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴, 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝘆𝗦𝗽𝗮𝗿𝗸, 𝗮𝗻𝗱 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝗻 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗹𝗶𝗸𝗲 𝗡𝗲𝗼𝟰𝗷 𝗮𝗻𝗱 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗼𝗳 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵𝘀 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗮𝘁𝗮 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀. 𝘀𝗸𝗶𝗹𝗹𝗲𝗱 𝗶𝗻 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗰𝘂𝘁𝘁𝗶𝗻𝗴-𝗲𝗱𝗴𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀), 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜), 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜, 𝗮𝗻𝗱 𝗚𝗿𝗮𝗽𝗵 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚) 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮 𝗳𝗼𝗿 𝗲𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴. e𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘂𝘀𝗶𝗻𝗴 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 𝘁𝗼 𝗲𝗻𝘀𝘂𝗿𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲, 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴.

Stackforce AI infers this person is a Data Engineering expert with a focus on real-time analytics and scalable solutions.

Location: Chennai, Tamil Nadu, India

Experience: 4 yrs

Skills

Data Engineering
Knowledge Graphs
Etl
Data Analytics
Cost Optimization
Real-time Data Processing

Career Highlights

Designed a Neo4j-based Knowledge Graph reducing reconciliation by 80%.
Built automated ETL pipelines cutting report generation time to under 1 hour.
Leveraged real-time data integration improving decision-making capabilities.

Work Experience

Kyndryl

Senior Data Engineer - Data & AI (11 mos)

Infosys

Data Engineer (3 yrs 1 mo)

Education

Bachelor of Engineering at Anna University Chennai

SIVA VISHNU A

AI Researcher

Chennai, Tamil Nadu, India4 yrs experience

Highly StableAI Enabled

Key Highlights

Designed a Neo4j-based Knowledge Graph reducing reconciliation by 80%.
Built automated ETL pipelines cutting report generation time to under 1 hour.
Leveraged real-time data integration improving decision-making capabilities.

Stackforce AI infers this person is a Data Engineering expert with a focus on real-time analytics and scalable solutions.

Contact

Skills

Core Skills

Data EngineeringKnowledge GraphsEtlData AnalyticsCost OptimizationReal-time Data Processing

Other Skills

pythonpandasNeo4jAirflowPower BIApache Spark StreamingAmazon Elastic MapReduce (EMR)Apache KafkaAWS RedshiftAWS EMRAWS DMSMicrosoft Power BIDSAAgenticAIProblem Solving

About

Experience

4 yrs

Total Experience

3 yrs

Average Tenure

11 mos

Current Experience

Kyndryl

Senior Data Engineer - Data & AI

Jul 2025 – Present · 11 mos · Chennai · Hybrid

Designed and implemented a Neo4j-based Knowledge Graph using Python, Cypher, and ETL pipelines to model complex asset relationships, reducing manual reconciliation by 80% and enabling real-time operational insights.
Built automated ETL pipelines using Python, Airflow, and pandas to ingest multi-source SCRT and infrastructure data, cutting monthly report generation time from days to under 1 hour, enabling faster decision-making for business stakeholders.
Developed machine-level MSU and utilization analytics using Neo4j Cypher queries and Python workflows, preventing overbilling from unused and decommissioned assets and delivering annual cost savings through license optimization.
Integrated multiple enterprise data sources using Python-based ETL and Neo4j graph modeling, creating a unified analytics layer that reduced data inconsistencies by 30% and improved reporting reliability for business users.
Automated inventory and licensing reconciliation workflows using Airflow orchestration, eliminating errors from decommissioned systems and improving audit readiness across large enterprise accounts.
Collaborated closely with business stakeholders and analytics teams to deliver actionable dashboards and reports via Power BI, supporting strategic decisions in IT asset management and enterprise cost optimization.

pythonpandasNeo4jAirflowPower BIData Engineering+1

Infosys

Data Engineer

Jun 2022 – Jul 2025 · 3 yrs 1 mo · Chennai, Tamil Nadu, India · Hybrid

Designed and implemented a scalable, fault-tolerant data pipeline using Spark Structured Streaming and Python on AWS EMR, processing over 0.5 million records daily, which reduced data processing time by 70% and enabled realtime analytics.
Leveraged Apache Kafka for real-time data integration, reducing data latency by 90%, resulting in timely insights that enhanced decision-making capabilities for business solutions.
Crafted a real-time data processing pipeline on AWS Redshift, achieving 100% data accuracy and eliminating data leaks, thus providing reliable insights for business users and improving operational efficiency.
Developed an incremental data pipeline using AWS DMS to transfer data from Oracle DB to Amazon S3, enabling daily updates with minimal latency and ensuring 99% data accuracy.
Designed a data pipeline for daily sales reports, leading to an 80% increase in sales by providing timely insights for quick decision-making and improved inventory management.
Optimized complex SQL queries, boosting performance by 25% for reporting services, cutting report generation time from hours to minutes, and contributing to a 20% reduction in operational costs.

Apache Spark StreamingAmazon Elastic MapReduce (EMR)Apache KafkaAWS RedshiftData EngineeringReal-time Data Processing