Software Developer Engineer

Arpit Singhal Software Engineer

Seattle, WA +1-224-***-**** ****************@*****.*** LinkedIn SUMMARY

Software Developer with 3+ years of experience building real-time data pipelines, scalable backend systems, and ML deployment workflows. Proficient in Java, Python, Go, and Big Data tools like Apache Flink, Kafka, and Airflow. Skilled in AWS (Lambda, Kinesis, S3, DynamoDB, SageMaker) and CI/CD automation using GitHub Actions. Improved system performance, reduced latency, and optimized data workflows across large-scale environments. Strong focus on clean architecture, test automation, and production-grade for data-driven applications. TECHNICAL SKILLS

Programming: Python, Java, Scala, C++, Golang, TypeScript, JavaScript, SQL, R, HTML Frameworks & Libraries: Spring Boot, Flask, Go-Kit, Apache Flink, Apache Spark, Scikit-learn, Gradient Boosting (GBM) Cloud & DevOps: AWS (Lambda, S3, Athena, Kinesis, DynamoDB, ECS), GitHub Actions, SageMaker, CI/CD Big Data & ETL: Kafka, Airflow, Snowflake, MongoDB, Redis, Databricks Testing & Monitoring: JMeter, Unit Testing, Data Validation, Alerting Systems Tools & Platforms: GenAI, ML DevOps, SQL Server, Tableau (assumed for dashboards), Flink Watermarks Data Engineering: Real-Time Data Pipelines, Data Lake Architecture, Schema Partitioning, Data Cleansing Collaboration: Agile, Cross-Functional Teaming, Dashboarding, Model Presentation PROFESSIONAL EXPERIENCE

OneBit (Fintech Startup) Dec 2024 – Current

Software Developer

• Designed and Enhanced full stack trading platform services in Java, Spring Boot, JPA/Hibernate, OKTA security, RESTful APIs, JavaScript and React, handling over 100K active requests and improving processing efficiency by 35%.

• Architected a streaming framework with Java, AWS Kinesis/KCL, DynamoDB and Spring Boot, handling 5M+ order events daily, sustaining sub-50ms latency under peak loads of 100K+ telemetry records/minute.

• Built serverless orchestration microservice architecture using Python, Event Bridge/API gateway, Lambda, Flask, and RDS, to process high-throughput trading logs; implemented failure handling and alerting to ensure zero data loss and rapid recovery.

• Revamped AWS S3 data lake using smart partitioning and compression methods, which Athena query latency by 30% and trimmed monthly storage expenses by 25%.

• Created ML model workflows in Scikit-learn, incorporating outlier detection and feature scaling, and deployed via SageMaker, enhancing prediction accuracy by 12% and cutting manual retraining effort by 80%.

• Set up integration and deployment pipelines (CI/CD) using GitHub Actions, integrating load testing with JMeter and unit validations

(Junit, PyTest), enabling zero-downtime releases across microservices and reduce the production issue by 20%. VMware LLC Jan 2022 – Dec 2022

Software Development Engineer

• Delivered scalable backend services in Java, Scala, and Golang for the core engine, optimizing ranking logic and cache layers. Improvements supported 90K+ DAUs, slashing personalization latency by 28%, and reducing cold-start issues.

• Formulated a distributed ingestion ecosystem using Java, Kafka, MongoDB, and Redis, processing mixed-structure datasets

(~3TB/month) from video telemetry, search logs, interactions for downstream consumption.

• Developed full-stack observability tools with TypeScript, React, and HTML, offering real-time visualizations of job metrics, lag status, and failures. This tool decreased incident debug time by 35%, and was adopted by four internal data teams.

• Automated reporting pipelines combining SQL joins, window functions, and R visualizations for churn analysis, A/B test reviews, and cohort comparisons. These insights helped the marketing team increase user re-engagement by 20% in pilot campaigns.

• Collaborated with ML Ops to deploy GBM models via REST APIs, embedding drift detection monitors and tracking version lineage using a custom registry. Stabilized prediction outcomes by 18%, especially for high-volume content scoring modules.

• Designed pipeline health checks to validate schema, volume, and timing thresholds, paired with granular alerts for runtime an omalies. These checks false alerts by 50% and sped up root cause triage during nightly failures by over 40 minutes. AbbVie Inc July 2019 – Feb 2021

Associate Software Engineer

• Accelerated API performance by 30% by reengineering backend services in Java and Spring Boot, introducing async processing for high- traffic endpoints used by over 50K daily active users.

• Streamlined event processing for 5M+ daily records by developing a real-time data pipeline using Apache Flink, Kafka, and Reids, enabling near-instant updates to customer dashboards and alerting systems.

• Reduced manual deployment time by 85% through full CI/CD automation using GitHub Actions, integrated with unit tests and SageMaker model deployment workflows, supporting rapid iteration across environments.

• Cut ETL job failures by 60% by restructuring Airflow DAGs, applying schema validation, and optimizing Snowflake partitioning logic, resulting in more stable ingestion of third-party financial data.

• Improved anomaly detection coverage by 40% by building a test suite in Python that combined unit testing, data validation, an d real- time monitoring hooks for early-stage ML outputs in SageMaker pipelines. EDUCATION

Master of Computer Science Dec 2024

Illinois Institute of Technology, IL, USA

Bachelors in Electronics and Communication Engineering May 2019 Jaipur Engineer College, Rajasthan, India

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