Jayanth Kota

Data Engineer

Oklahoma City, Oklahoma, United States4 yrs experience

Key Highlights

Improved pipeline efficiency by 40% in financial data processing.
Reduced infrastructure costs by 30% through AWS migration.
Enhanced query performance by 35% for financial analytics.

Stackforce AI infers this person is a Data Engineer specializing in Fintech and E-commerce data solutions.

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Skills

Core Skills

Data EngineeringAwsE-commerce Data ProcessingCloud Data PlatformsCloud Data Solutions

Other Skills

ELT/ETLData WarehousingSpark Structured StreamingKinesisCI/CDData Qualitymarketing data analyticsevent-data architectureAWS S3KafkaSparkData ModelingData Governancecustomer behaviour analyticssales data pipelines

About

1. Data Engineer with 6 years of experience building scalable batch and real-time data pipelines across financial/banking and e-commerce domains. 2. Specialise in AWS-based cloud data platforms, Big Data processing with Spark/Databricks, and streaming systems using Kafka/MSK and Kinesis. 3. I’ve delivered measurable impact such as improving pipeline efficiency (40%), lowering infrastructure costs (30%), enhancing query performance (35%), and reducing fraud model false positives (25%). 4. Translated business problems into reliable, production-ready data systems with strong governance, security, and CI/CD best practices.

Experience

4 yrs

Total Experience

2 yrs

Average Tenure

Current Experience

Freddie mac

Data Engineer

May 2025 – Present · 1 yr 1 mo · United States · Remote

1. Built and optimized ETL/ELT pipelines for structured + semi-structured data from core banking systems and third-party providers, improving pipeline efficiency by 40%.
2. Designed scalable ingestion/processing pipelines for financial datasets (transactions, customer profiles, trades, market feeds, risk data).
3. Developed enterprise warehouse data models (Star schema + lineage) enabling regulatory reporting, fraud detection, credit risk, and revenue analytics.
4. Implemented data quality, reconciliation, and validation controls to ensure financial accuracy and compliance.
5. Delivered real-time streaming pipelines using Spark Structured Streaming + Kinesis/MSK (Kafka) for fraud monitoring and risk alerting.
6. Migrated legacy ETL workloads to AWS + Snowflake, reducing infrastructure cost by 30%.
7. Improved query performance by 35% through optimized modeling for financial instruments, derivatives, and risk portfolios.
8. Established CI/CD for data workflows using Git, Docker, Jenkins, Kubernetes, and AWS CodePipeline, improving deployment speed by 30% andPartnered with quant and data science teams to deploy fraud and credit risk ML workflows, reducing false positives by 25%.

ELT/ETLData WarehousingSpark Structured StreamingKinesisAWSCI/CD+2

Best buy

Data Engineer

Apr 2024 – Apr 2025 · 1 yr · United States · Remote

1. Designed scalable ETL/ELT pipelines to process large-scale e-commerce data including orders, payments, product catalog, pricing, and customer interactions.
2. Built data ingestion frameworks for collecting data from web applications, mobile apps, POS systems, and third-party retail platforms and Developed real-time data streaming pipelines for clickstream, browsing behavior, and order lifecycle tracking using Kafka and Spark.
3. Implemented a centralized AWS S3 data lake with raw, curated, and analytics layers for structured and semi-structured retail data.
4. Created optimized data models to support sales performance dashboards, customer journey analytics, and marketing campaign insights.
5. Implemented real-time event pipelines using Kafka/Kinesis + Spark Structured Streaming for clickstream and order lifecycle analytics.
6. Automated deployments for pipelines and Databricks notebooks using GitHub Actions + Kubernetes.
7. Worked with product/marketing/merchandising/ops teams to convert business requirements into scalable data solutions.
8. uilt datasets for pricing optimization, demand forecasting, and seasonal sales trend analysis, Reduced manual data processing by automating batch workflows and scheduling jobs & Ensured data governance, access control, and secure handling of customer data.

marketing data analyticsevent-data architectureAWS S3KafkaSparkData Engineering+1

Intuit

Data Engineer

Aug 2021 – Aug 2023 · 2 yrs · United States · Remote

1. Built scalable cloud data platforms processing application/system/user interaction data.
2. Developed batch + streaming pipelines using Kinesis/MSK + Spark Structured Streaming for real-time analytics and product insights.
3. Designed Star/Snowflake dimensional models for product usage, customer behavior, and operational metrics.
4. Implemented data quality + anomaly detection to improve reliability of data assets.
5. Optimized Spark and Databricks workloads to improve performance, reduce latency, and handle high-throughput data processing. Designed dimensional data models (Star/Snowflake schema) for product analytics, usage metrics, and operational reporting.
6. Created curated datasets used by data analysts and data scientists for reporting, experimentation, and ML models. Automated data workflows using Apache Airflow DAGs for scheduled and event-driven processing.
7. Worked closely with product managers, data scientists, and analytics teams to deliver high-quality data assets. Enabled self-service analytics by building clean, well-documented datasets for BI teams.
8. Reduced manual intervention by automating data ingestion, transformation, and deployment processes. Built scalable data solutions supporting product performance tracking and customer engagement insights.

KinesisSparkData QualityData ModelingData EngineeringCloud Data Platforms

Virtusa

Data Engineer

Jul 2019 – Jul 2021 · 2 yrs · Hyderabad, Telangana, India · Hybrid

1. Built AWS cloud-native ingestion and transformation pipelines (Glue, EMR, RDS, Redshift, Spark, S3) for high-volume data.
2. Designed S3-centric data lake layers for downstream analytics and consumption.
3. Implemented validation, reconciliation, and multi-stage data quality checks to ensure accuracy and completeness.
4. Provided production support (monitoring, troubleshooting failures, performance optimization) to meet SLAs.
5. Ensured governance and security using RBAC, IAM, KMS, encryption, logging, and monitoring.

AWSData QualityData GovernanceData EngineeringCloud Data Solutions