Vinay Kumar Chandra

Data Engineer

Syracuse, New York, United States3 yrs 9 mos experience

Key Highlights

Architected scalable cloud data platforms for major firms.
Reduced data latency from hours to seconds for fraud detection.
Delivered robust data systems ensuring 100% data integrity.

Stackforce AI infers this person is a Data Engineer specializing in Fintech and SaaS with expertise in cloud data platforms.

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Skills

Core Skills

Data EngineeringCloud Data Platforms

Other Skills

AWSSQLPySparkData QualityData TransformationData LineageData AnalyticsPythonData MigrationData ValidationEasily AdaptableRisk AnalyticsExploratory Data AnalysisAnalytical SkillsBusiness Analytics

About

Data Engineer with 3+ years of experience architecting scalable cloud data platforms and Generative AI infrastructure. Proficient in the modern data stack (AWS, Snowflake, dbt, Airflow) and AI-native tools (LangChain, Vector DBs), with a strong background in real-time streaming and RAG pipeline development. Demonstrated success in modernizing legacy systems and optimizing compute costs for global leaders in Fintech and SaaS. Skilled in building robust, self-healing data systems that ensure 100% data integrity for advanced analytics an machine learning applications.

Experience

3 yrs 9 mos

Total Experience

Average Tenure

Current Experience

Hubspot

Data Engineer

Jul 2025 – Present · 11 mos · United States · Remote

Built scheduled API ingestion jobs to pull product activity logs, CRM event records, and marketing platform exports from HubSpot application services, landing 8 TB of raw data monthly into AWS S3 for downstream analytics and reporting consumption.
Developed PySpark and SQL transformation routines to reshape customer activity tables, reconcile user identifiers across internal services, and produce structured warehouse datasets serving analysts across marketing performance and product usage reporting.
Introduced data lineage tracking and automated validation checks comparing pipeline output against source application logs, identifying incomplete event batches early and reducing analyst-reported data corrections by 40% during monthly reporting cycles.
Redesigned high-frequency reporting queries to run against pre-aggregated warehouse tables instead of scanning multiple operational sources, reducing dashboard refresh times by 16% at peak load across product and marketing teams.
Delivered a consolidated cross-channel engagement dataset processing 500M+ events monthly now used by three internal reporting teams for campaign performance and product usage analysis, eliminating weekly spreadsheet reconciliation steps.
Partnered with analytics engineers, product managers, and marketing analysts to align metric definitions and dataset requirements before pipeline changes entered shared scheduled workflows.

AWSSQLPySparkData QualityData TransformationData Lineage+3

Genpact

Data Engineer

Jan 2022 – Dec 2023 · 1 yr 11 mos · Bengaluru, Karnataka, India · On-site

Extracted 4+ TB of monthly procurement, billing, and transaction records from enterprise ERP databases and scheduled file exports, loading source datasets into cloud storage (AWS S3) to support finance and operations reporting.
Built Python and SQL transformation pipelines to standardize vendor and invoice identifiers across raw ERP tables, producing structured Silver-layer datasets that allowed analysts to review spending patterns in a single source rather than joining multiple operational extracts manually.
Reworked partition logic and table structures on heavy monthly batch jobs, cutting finance reporting pipeline processing time by 18% equivalent to 3.5 hours saved per monthly close cycle without additional infrastructure spend.
Implemented automated data quality checks validating row counts, null rates, and referential integrity on vendor transaction feeds, reducing data errors escalated to finance leadership by 30% quarter-over-quarter.
Delivered a consolidated vendor reporting dataset combining vendor transactions, billing records, and purchase orders that became the default data source for Tableau dashboards replacing six independently maintained spreadsheet reports across procurement teams.
Coordinated with finance analysts and reporting specialists on metric definitions and pipeline delivery timelines to ensure datasets were available before weekly operational reviews.

AWSPythonSQLData QualityData TransformationData Analytics+2

Wipro

Data Engineer

Jan 2021 – Dec 2021 · 11 mos · Bengaluru, Karnataka, India

Engineered a real-time event aggregation pipeline using AWS Kinesis and Lambda to process high-volume banking transactions, successfully reducing data latency from hours to seconds for critical fraud detection and account reconciliation.
Migrated eight years of historical customer data from legacy on-premise systems to AWS Redshift using AWS Glue, optimizing ETL workflows to handle 2TB of financial records with zero data loss during transition.
Developed robust Python-based API connectors to link core banking systems with external Fintech applications, ensuring seamless data synchronization and improving third-party data availability by 40% for customer-facing digital banking services.
Implemented automated data validation frameworks using Python to audit sensitive PII across migrated datasets, achieving 100% compliance with financial regulations and eliminating manual verification overhead for three million customer records.
Optimized complex SQL queries for regulatory reporting within AWS Redshift, reducing query execution time by 35% and enabling faster generation of daily liquidity reports required by internal financial auditing teams.

AWSPythonSQLData MigrationData ValidationData Engineering+1