Jaswinder Singh

About Jaswinder Singh

As a Software Engineer with experience spanning financial services, healthcare, and enterprise systems, I design and implement scalable, high-performance solutions that drive operational excellence and data integrity at scale. I specialize in backend development, data engineering, and cloud-native architectures, bringing a strong foundation in computer science and a proven ability to deliver in fast-paced, high-stakes environments.At Deutsche Bank, I lead the development of automated data pipelines and cloud orchestration solutions using Python, Scala, Airflow, Kafka, and Google Cloud Platform. My work includes:* Building resilient Kafka-to-BigQuery replay systems with dynamic GitHub-triggered workflows* Optimizing Airflow DAG performance by migrating from CLI to secure Python APIs* Enhancing data quality pipelines through advanced config parsing and validation frameworks* Implementing test reporting infrastructure with Maven, Surefire, and JaCoCo for continuous quality assurancePreviously, I contributed to backend systems at CVS Health, improving data accuracy and reducing search latency for critical healthcare applications. At LBi Software, I delivered data automation tools for professional sports organizations, reducing manual overhead and accelerating analytics.With academic training in artificial intelligence, algorithms, systems, and software security, I blend deep technical skills with a clear understanding of software architecture, data governance, and agile delivery.Key Strengths: End-to-end system design across distributed and cloud environments Data pipeline engineering and backend optimization Clean code, test-driven development, and DevOps integration Cross-functional collaboration with product, data, and infrastructure teams

Experience

1. Swe

   Deutsche Bank

   Jul 2024 — Present · Cary, NC, US

   Rotation_2• Airflow DAG for Kafka-to-BigQuery Replay with Github Workflow Automation: Built an Airflow DAG to replay Kafka events into BigQuery for a specified time range. Automated DAG lifecycle (trigger, pause, resume, terminate) using GitHub Workflow Actions.• Automated Kafka-to-BigQuery Completeness Check via Cloud Composer: Updated the project to bring the expected config file format in line with current config file format. Created a new configuration class to support the parsing of the updated config format, dynamically mapping it to the legacy structure. Deployed the project JAR to the Dataproc GCS bucket to enable DAG execution of the validation job as part of the pipeline workflow.• Optimized Pipeline Performance by Migrating from CLI to Python API: Replaced gcloud CLI calls with Python client libraries to optimize performance and reduce resource usage in DAGs. Retrieved secrets securely via Secret Manager API and handled them using tempfile and subprocess. Improved maintainability, reduced latency, and enhanced pipeline security.• Integrated Maven Site Reporting for Quality Metrics: Implemented Maven site reporting to generate detailed test and coverage reports. Integrated Surefire, Failsafe, and JaCoCo plugins for unit and integration test insights. Reorganized and renamed test files to ensureproper classification and aggregationRotation_1• Kafka Publishing Functionality Testing: Developed a stored procedure to verify Kafka message publishing functionality by initiating change detection through record updates across relevant tables and confirming successful message delivery.• Data Validation and Update: Developed SQL scripts to first validate the existence of vault records matching update criteria, ensuring the necessity of the update. Executed the update script and implemented a validation process to confirm the updates were successfully applied.

Education

• Stony Brook University

  Bachelor's degree, Computer Science

  2020 — 2024