Resume Tour

A journey through backend and data engineering

My work has taken me from Python services for point-of-sale systems to AWS data pipelines, backend automation, and analytics APIs for CortexAI.

  1. Jul 2021 - Sep 2024
    Capgemini Senior Software Analyst (Python Developer) PythonAWS LambdaAPI GatewayS3CloudWatchSQL

    Built and maintained Python backend services for NPOS order and payment flows. Integrated AWS Lambda, API Gateway, S3, and CloudWatch; optimized SQL queries and supported banner promotion logic and real-time interface updates.

  2. Oct 2024 - Feb 2025
    Accenture Python Developer (Data Engineer) AWS GluePySparkS3FlaskPyTestJenkins CI/CD

    Built AWS Glue and PySpark ETL pipelines for Food & Paper data from S3, processing 5M+ records each day and reducing runtime by 25%. Flask validation services improved data accuracy to 99.8%; Jenkins CI/CD automation reduced deployment time by 40%.

  3. Jun 2025 - Oct 2025
    Dell Technologies Python Developer | via UST Global FastAPI / FlaskAsync PythonAWS GluePySparkKafkaRedisMongoDBGitLab CI/CD

    Built async FastAPI/Flask services with background schedulers, retries, and health checks. Developed AWS Glue/PySpark pipelines for 5M+ daily records from S3 to Redshift/Athena, reducing runtime by 25%. Also worked on Redis caching, Kafka-to-MongoDB flows, automated tests, GitLab CI/CD, and structured logging.

  4. Nov 2025 - Jul 2026
    Genpact Python Developer PythonNetskope REST APISecure authenticationCSV reportingGit workflow

    Automated Netskope REST API compliance reporting across 10,000+ devices. Used environment variables and CLI parameters for authentication and configurable filters, then transformed responses into structured CSV reports. Followed a fork, commit, and pull-request Git workflow.

  5. 10 Jul 2026 - Present
    eBay via Prescience Python Backend Developer | CortexAI PythonREST APIsHive SQLParquetRedisAnalytics / BI

    Build Python services and REST APIs for CortexAI analytics dashboards. Optimize Hive SQL queries, retrieve Parquet datasets from Google Drive for retail year/week reporting, add Redis caching, and validate data mappings across business segments.