Senior Python Data Engineer with AWS & Airflow Expertise

Name: Rajasri

Email: **********@*****.***

Phone No: 972-***-****

SUMMARY PROFESSIONAL:

Python Data Engineer with 7 years of experience building scalable ETL pipelines, event driven data systems, and cloud native applications on AWS.

Strong background in batch and real-time data processing, workflow orchestration, and high-volume analytics using Airflow, Kafka, and Python data stacks

Experienced in designing REST APIs, distributed microservices, and fault-tolerant architectures that support large-scale user activity and transaction platforms

Strong command of SQL and NoSQL databases, including PostgreSQL, MySQL, MongoDB, DynamoDB, and Redis, with real-world experience optimizing queries and indexes.

Reduced manual effort by automating repetitive document and data handling tasks across GCP and SharePoint ecosystems

Strong experience designing non-trivial systems: microservices, event-driven pipelines, and fault-tolerant distributed services.

Supported identity lifecycle processes including access provisioning, updates, and deactivation across systems

Hands-on experience with microservices architecture, including service decomposition, API versioning, and inter-service communication using Kafka and cloud messaging services.

Skilled in JavaScript/TypeScript-based frontend integration, containerized deployments using Docker, and building secure, modular, and maintainable services following modern MVC architecture and development best practices.

Experience working with AWS services, Airflow-style scheduling concepts, and CI/CD pipelines to support automated deployments and reliable data workflows.

Strong CI/CD experience (GitHub Actions/Jenkins/GitLab CI) integrating dependency checks, security scanning, and automated releases.

Develop responsive application UI components using Angular and TypeScript, integrating seamlessly with backend services.

Optimized existing scripts, reducing execution time and improving reliability

Experience managing and supporting Airflow cluster operations, including environment configuration and job scheduling.

Hands-on experience supporting application teams by building and maintaining workflow automation solutions.

Investigated verbal and written feature requests, translating business requirements into precise technical specifications and implementation plans for backend and frontend development.

Skilled in developing automation tools for operational monitoring, alert handling, and system health checks using Python integrated with cloud platforms and messaging service

Solid experience implementing asynchronous processing using Celery, RabbitMQ, Kafka consumers, and Python asyncio for background jobs and data pipelines.

Strong background in data engineering concepts, including ETL pipelines, batch and streaming data processing, and integration with analytics platforms.

Practical exposure to machine learning workflows, including model integration, inference APIs, and data preprocessing using Pandas, NumPy, and Scikit-learn.

Hands-on experience in secure coding practices, implementing OAuth2, JWT authentication, role-based access control, and data encryption standards.

Strong understanding of software development lifecycle (SDLC), Agile/Scrum methodologies, sprint planning, and cross-functional collaboration.

Experience working closely with product managers, QA teams, DevOps engineers, and business stakeholders to translate requirements into production-ready solutions.

Proven ability to write high-quality unit, integration, and end-to-end tests using PyTest, unittest, and mocking frameworks.

Skilled in CI/CD automation, integrating Python projects with GitHub Actions, Jenkins, and cloud deployment pipelines.

Comfortable working in regulated environments, adhering to compliance, audit, logging, and monitoring requirements.

Demonstrated ability to mentor junior developers, perform code reviews, and enforce engineering best practices.

Strong problem-solving mindset with a focus on building resilient, fault-tolerant systems that scale under real business load.

Collaborated with cross-functional teams to translate business requirements into AI workflows, including prompt design, retrieval strategy tuning, and output validation.

Built end-to-end test automation using Playwright, covering UI, API, and cross-browser scenarios with reliable CI execution.

Reduced manual effort by automating repetitive document and data handling tasks across GCP and SharePoint ecosystems

Continuously upgrading technical expertise to stay aligned with modern Python ecosystem, AI integration trends, and cloud-first architectures.

Managed machine identities including service accounts, API keys, and tokens for secure inter-service communication in cloud environments

Proven ability to collaborate with data engineers, DevOps teams, and business stakeholders to deliver reliable automation and data solutions.

Demonstrated ability to own features end-to-end, from requirement analysis and system design through development, deployment, monitoring, and post-production support in real enterprise environments.

Strong experience working with cross-regional and onshore–offshore teams, effectively communicating technical decisions, risks, and trade-offs to both technical and non-technical stakeholders.

Supported reduction of mean time to remediate (MTTR) for security issues through automation and CI/CD enforcement.

Proven track record of stabilizing existing systems, reducing production incidents, and improving overall platform reliability through refactoring, monitoring, and proactive performance optimization.

Worked in regulated environments, ensuring traceability, logging, and documentation required for security and compliance reviews.

TECHNICAL SKILLS:

Programming Languages: Python (automation, scripting, APIs), Shell Scripting,Flask, Django

Cloud Platforms: AWS (IAM, Lambda, EC2, S3, RDS, EKS),Google Cloud Platform (IAM, Cloud Functions, Cloud Run – basic exposure), Microsoft Azure (basic exposure to identity & AI services)

Identity & Access Management (IAM): IAM Roles & Policies, RBAC, Least Privilege Access,OAuth2, JWT, API Authentication

Service Accounts, Token-based Authentication

Python Frameworks & Libraries: FastAPI, PySPark, Django, Pandas, NumPy, Celery, SQL Alchemy, Pydantic, Requests,

AI / GenAI: OpenAI / Azure OpenAI (API integration), AWS Bedrock, Large Language Models (LLMs)

Databases: PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, Amazon Redshift

Data Engineering & ETL: Python ETL Pipelines, Data Transformation, Batch Processing, Data Validation, Data Ingestion, Workflow Automation

Testing & Quality: Unit testing, Integration testing, PyTest, Mocking frameworks

CI/CD & Version Control: Git, GitHub Actions, Jenkins

Data & Analytics: ETL Pipelines, Data Modeling, Batch & Streaming Processing, Feature Engineering (basic)

Monitoring & Logging: CloudWatch, ELK Stack, ETL Failure Troubleshooting, Runbook Automation, Logging & Observability

PROFESSIONAL EXPERIENCE:

Client: WEX Inc, Portland, ME May 2025 – Till Date

Role: Full Stack Python Engineer

Responsibilities:

Developed end-to-end automation scripts in Python for device setup, data processing, and workflow optimization

Built batch and near real-time data workflows with AWS Lambda, SQS, SNS, and Kafka, enabling event-driven streaming pipelines for analytics and AI enrichment.

Automated Excel-based workflows using pandas and openpyxl for large-scale data processing

Worked with AWS IAM to define roles, policies, and least-privilege access for services and automation pipelines

Built and optimized vector-based retrieval workflows using embedding generation, similarity search, and context ranking to improve response relevance in LLM applications.

Processed large datasets using Python (Pandas, PySpark) for reporting, behavioral analysis, and downstream data science use cases.

Developed RESTful microservices using FastAPI and Flask to expose media and analytics data services with JWT security and schema validation.

Containerized services with Docker and deployed on AWS EKS, implementing CI/CD via GitHub Actions and improving release stability and scalability.

Implemented async Python services using AsyncIO and FastAPI, reducing request latency and improving system throughput under concurrent load.

Automated operational monitoring and alert handling using Python scripts integrated with Slack, Jira, and CloudWatch, improving visibility of ETL failures and system health.

Applied Redis caching for embeddings, prompt responses, and frequently accessed retrieval results to reduce inference latency and cost.

Designed automation scripts for data validation, batch processing, and system monitoring, reducing manual effort and errors.

Secured application secrets using environment-based configurations and cloud-native secret storage practices

Collaborated with frontend engineers to deliver secure APIs supporting TypeScript/React clients, implementing JWT authentication, pagination, and structured error handling.

Implemented asynchronous processing pipelines using Celery for batch LLM inference, document processing, and scheduled reporting.

Identified bottlenecks and inefficiencies in legacy pipelines, refactoring workflows to improve performance and reduce failures.

Participated in architecture discussions around prompt engineering strategies, vector store design, context window optimization, and scalability of GenAI systems.

Monitored GenAI-enabled production services using CloudWatch, tracking inference latency, error rates, and system health to proactively resolve issues.

Collaborated with DevOps and security teams to enforce compliance, audit logging, and secure deployment practices

Environment: Python, Apache Airflow, FastAPI, React, Kafka, AWS (SQS, SNS, Lambda, CloudWatch), Amazon Redshift, Apache Kafka, Flask, Lang Chain, OpenAI / Azure OpenAI, RAG, Vector Embeddings, RESTful APIs, Kafka, Docker, Kubernetes (EKS), Redis (Inference & Embedding Cache), CI/CD with GitHub Actions, JWT Authentication, IAM, CloudWatch

Client: Arkansas Blue Cross Blue Shield, Little Rock, AR Oct 2024 – April 2025

Role: Python Developer (AI / Backend)

Responsibilities:

Developed Python ETL pipelines and automation scripts to ingest and transform large healthcare claims datasets for analytics and reporting platforms.

Integrated Python applications with SharePoint using REST APIs for document management and workflow automation

Implemented agent-like workflows using LangChain-based orchestration patterns for multi-step reasoning, tool usage, and context-aware response generation.

Built batch and asynchronous processing workflows using Celery, RabbitMQ, and AWS services, supporting high-volume background jobs and inference tasks.

Implemented LLM-assisted logic and rule-based validation models to improve claims processing accuracy and reduce manual review efforts.

Built Python automation to map new CVEs (from OSV/NVD feeds) to affected services by correlating vulnerability data with SBOM dependency graphs.

Designed and optimized PostgreSQL queries to efficiently handle claims and member data used in both transactional workflows and AI context retrieval.

Worked on rule-based validations and assisted in AI-assisted logic to improve claims accuracy and reduce manual review effort.

Implemented role-based authentication and authorization to ensure secure access to healthcare data and AI-enabled APIs.

Worked with DevOps teams to support CI/CD pipelines for Airflow deployments and Python services.

Optimized complex PostgreSQL queries and data models, improving claims data retrieval performance for analytics workloads.

Created runbooks and operational documentation for ETL troubleshooting, deployment procedures, and incident response processes.

Participated in Agile ceremonies and collaborated with data engineers and QA teams to ensure reliability of production data pipelines.

Deployed and maintained Python services on AWS EC2 and RDS with guidance from senior engineers, ensuring reliability and scalability.

Added structured logging and monitoring to support debugging, audit trails, and transparency of AI-assisted features.

Collaborated with QA teams on unit and integration testing to validate API behavior and LLM outputs.

Refactored existing Python code to improve readability, performance, and maintainability.

Participated in Agile ceremonies and contributed to sprint tasks related to backend and AI feature development.

Assisted with Dockerizing services and supporting CI/CD pipelines for consistent deployments.

Provided production support, investigating issues and implementing fixes under senior guidance.

Environment: Python, Django, Flask, Django REST Framework, new CVS (OSV/NVD),OpenAI / Azure OpenAI (API integration), LangChain (basic), REST APIs, PostgreSQL, AWS (EC2, S3, RDS), Celery, RabbitMQ, Docker, Jenkins, Git, Linux

Client: Aristocrat IT Solutions, India Oct 2020 – July 2024

Role: Software Engineer (Python)

Responsibilities:

Developed and maintained Python-based backend systems supporting enterprise web applications and internal business platforms across multiple client engagements.

Built and deployed Python automation workflows on Google Cloud Platform using Cloud Functions and Cloud Run for scalable task execution

Designed and implemented RESTful APIs using Flask and Django, enabling communication between frontend applications, mobile clients, and third-party services.

Created and managed relational and NoSQL data models using MySQL and MongoDB, ensuring efficient data storage and retrieval for transactional and analytical use cases.

Built ETL pipelines and data processing scripts using Python to clean, transform, and load business data into reporting and analytics systems.

Implemented authentication, authorization, and session management mechanisms to support multi-user and role-based access systems.

Leveraged Pandas and NumPy to perform data analysis, aggregation, and preprocessing for dashboards and operational reports.

Integrated third-party APIs and data sources, implementing error handling and retry mechanisms to ensure reliable data processing.

Supported AWS deployments and DevOps processes, assisting with infrastructure configuration, CI/CD automation, and production monitoring.

Integrated third-party APIs, external services, and payment gateways, handling data validation, error handling, and retries.

Deployed Python applications on AWS infrastructure, assisting with environment setup, configuration, and performance tuning.

Wrote unit tests and participated in debugging efforts to resolve functional and performance-related issues in production systems.

Actively participated in Agile development processes, including sprint planning, daily stand-ups, and sprint demos.

Identified performance bottlenecks and refactored inefficient code paths to improve application responsiveness.

Maintained technical documentation for backend services, APIs, and data workflows.

Supported client-facing demonstrations and provided technical explanations of system functionality.

Contributed to the development of reusable Python libraries and internal tools, improving development efficiency across teams.

Environment: Python, Flask, Django, MySQL, MongoDB, AWS, Pandas, NumPy, Git, Linux

Client: Zemoso Technologies, India Jan 2019 – Sep 2020

Role: Junior Python Developer

Responsibilities:

Assisted in the development of backend modules using Python and Flask, supporting internal tools and early-stage client applications.

Implemented REST APIs to expose backend functionality to frontend teams, ensuring proper request validation and response handling.

Worked on database interactions using SQLite and MySQL, writing queries to retrieve, insert, and update application data.

Developed Python scripts to process and format data for internal reporting and analytics use cases.

Fixed bugs and enhanced existing Python code under the guidance of senior developers.

Participated in feature development, gradually taking ownership of small modules and components.

Learned and applied Python coding standards, modular design principles, and basic design patterns.

Supported application deployments and configurations on Linux-based environments.

Wrote basic unit tests to validate application functionality and reduce regression issues.

Assisted in maintaining application logs and troubleshooting runtime errors.

Collaborated with frontend developers to resolve API integration issues.

Participated in peer code reviews, learning best practices and improving code quality.

Updated technical documentation and inline code comments for clarity and maintainability.

Assisted senior engineers during performance testing and optimization activities.

Handled minor production support tickets and issue tracking.

Environment: Python, Flask, SQLite, MySQL, Git, Linux

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