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Nishitha - Senio python developer

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Location: Dallas, Texas, USA

Relocation: Yes

Visa: GC

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Nishitha Python Developer PH: +1(732)- 510- 0599 Email: [email protected] PROFESSIONAL SUMMARY: Around 10+ years of experience as a Python Developer, proficient coder in multiple languages and environments including Python, AWS and SQL. Expert in using a range of AWS cloud platform services including Glue, Redshift, S3, EMR, and EC2. Fluent programming experience with Scala, Java, Python, SQL, and R. proficiency with SQL and MongoDB in addition to working with Spark RDD, Data Frame API, Data Set API, Data Source API, Spark SQL, and Spark Streaming. Written PySpark code in AWS Glue to combine data from several tables and in AWS Glue data catalog to add metadata table definitions using Glue Crawler. Created Pipelines in ADF using Linked Services/Datasets/Pipeline/ to Extract, Transform and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool and backwards. Developed numerous graphs for efficient corporate decision-making using Python's matplot package and the AWS data pipeline for data extraction, transformation, and loading. Experienced in Data Analytics Engineer with a strong background in programming, algorithms, predictive modeling, machine learning and data insights Built Python-based stream processing logic to consume Kafka topics, apply transformations, and load enriched data into databases like Redshift, PostgreSQL, and Elasticsearch Design and implement database solutions in Azure SQL Data Warehouse, Azure SQL Involved in building Data Models and Dimensional Modeling with 3NF, Star, and Snowflake schemas for OLAP and Operational data store (ODS) applications. Worked on NoSQL databases like MongoDB, Document DB and Graph Databases like neo4j. In-depth knowledge of Snowflake Database, Schema and Table structures. knowledge on how to explore, visualize, and manipulate data using Data Bricks notebooks. knowledgeable about building PySpark code for ETL pipelines that convert, clean, and aggregate data in Data Bricks clusters. Strong understanding of real-time data processing, Spark Streaming on AWS S3 for transformations, aggregations, building data models, and persisting in HDFS. Developed various Azure Data factory pipelines using SQL, and Python for performing Data Transformations. Utilized Kafka to consume Extensible Markup Language (XML) messages and processed them using Spark Streaming. Involved in creating new reports and modifying existing reports using Power BI. Extensive experience using SQL, Python, PySpark, and Databricks for analyzing the Big Data as per the requirement Worked on Data Science projects and created application workflows in the Hadoop cluster to ingest the data from varied data sources including Oracle and MySQL to HDFS. Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the Sql Activity. Experience in using Scikit-Learn and Stats models in Python for Machine Learning and Data Mining. TECHNICAL SKILLS: Programming Languages: Python, JavaScript, Shell Script, TypeScript, SQL, PL/SQL Frameworks & Libraries: Django, FastAPI, Flask, React, Redux, Angular, Vue.js, SQL Alchemy, Pandas, NumPy, Scikit-learn, TensorFlow Databases: PostgreSQL, MySQL, MongoDB, SQL Server, AWS Aurora, Redis APIs & Web Services: RESTful APIs, GraphQL, SOAP, WebSockets, OAuth 2.0, JWT Cloud Platforms & Services: AWS (EC2, S3, RDS, Elastic Beanstalk, Lambda, API Gateway, CloudWatch, CloudTrail, CloudFormation), Azure (Azure AD, Azure Functions, Azure App Service, Azure DevOps, Azure Data Factory), Google Cloud CI/CD & DevOps: Jenkins, Bitbucket, Git, GitHub, Terraform, Ansible, ANT, WebLogic, Azure DevOps Infrastructure & Containerization: Docker, Kubernetes, OpenShift, Nginx, RabbitMQ, Celery, Apache Kafka Monitoring & Logging: AWS CloudWatch, ELK Stack (Elasticsearch, Logstash, Kibana) Testing & Code Quality: PyTest, Mockito, Swagger, Postman, SonarQube, Test-Driven Development (TDD) Authentication: OAuth 2.0, JWT, Role-Based Access Control (RBAC), Data Encryption EDUCATION: Bachelor in Technology, Anna University, India ,2014 PROFESSIONAL EXPERIENCE: Client: Fidelity Investments, Westlake, TX Oct 2023 - Till Date Role: Python Developer Responsibilities: Automate different workflows, which are initiated manually with Python scripts and Unix shell scripting. Utilized SQL and MongoDB in conjunction with Spark RDD, Data frame API, Data Set API, Data Source API, Spark SQL, and Spark Streaming. Managed datasets using Panda data frames and MySQL, queried MYSQL database queries from python using Python-MySQL connector and MySQL dB package to retrieve information. Deployed serverless Python applications using AWS Lambda, triggered via S3 events and API Gateway for real-time data processing. Designed and implemented RESTful APIs using Python (Flask/FastAPI) and .NET Core to support enterprise-level financial data workflows. Collaborated with data scientists to build and deploy LLM- and RAG-driven automation pipelines, enhancing internal search and insight generation capabilities. Developed React-based user interfaces integrated with backend APIs for real-time analytics and dashboard visualizations. Managed Azure SQL schema design, query optimization, and integration with backend data services. Deployed scalable microservices on Azure App Services and automated builds through Azure DevOps CI/CD pipelines. Implemented secure authentication flows using Azure Key Vault and OAuth 2.0 for backend and front-end integration. Worked on hybrid full-stack systems using Python, C#, and TypeScript, ensuring high performance and maintainability. Participated in Agile sprint planning and peer code reviews, driving continuous improvement and delivery quality. Built a real time anomaly detection service in Python using scikit learn, flagging outliers in transaction streams and alerting downstream processes Developed and productionized a predictive risk scoring model leveraging Pandas and NumPy pipelines to quantify portfolio volatility for traders Collaborated with data scientists to integrate Jupyter based data exploration notebooks into automated ETL workflows Developed real-time data ingestion pipelines in Python using Apache Kafka, consuming and processing millions of messages per day for analytics and monitoring. Maintained scalable microservices in Python, deployed on Kubernetes clusters, ensuring high availability and fault tolerance. Implemented data pipelines using Python and MongoDB, processing large volumes of data for real-time analytics Built data pipelines to ingest and transform data from multiple sources, including third-party APIs, and load it into MongoDB and relational databases. Automated CI/CD pipelines using GitHub Actions/Jenkins for Python applications, integrating with Kubernetes via Helm charts and custom deployment scripts. Built Python-based Lambda functions for ETL workflows, reducing manual intervention and optimizing data flow between S3, RDS, and Redshift. Wrote Python scripts to automate data ingestion and transformation pipelines using S3, Redshift, and Glue, improving reporting latency by 45%. Implemented Apache Airflow for authoring, scheduling, and monitoring Data Pipelines Developed ETL processes to extract data from various sources, transform it using Python scripts, and load it into MongoDB and relational databases. Conducted performance tuning and optimization of SQL queries, stored procedures, and batch processes to enhance data processing efficiency Maintained end-to-end data analytics pipelines, from data collection and preprocessing to modeling and visualization, using tools such as Python, R, SQL, and data analytics frameworks like Pandas, NumPy, and TensorFlow Integrated MongoDB with other data storage solutions such as Amazon S3 and Redshift, enabling unified data analytics. Built Lambda functions in Python to process real-time data streams from Kinesis and store results in S3 and Redshift, reducing data latency by 60%. Developed a fully automated continuous integration system using Git, Jenkins, MySQL and custom tools developed in Python and Bash Built automated Python ETL pipelines to extract data from APIs, transform it, and load it into AWS RDS, reducing manual reporting time by 70%. Environment: Python 3.9, Spark, Spark-Streaming, Spark SQL, AWS EMR, mapR, HDFS, Apache Kafka, Sqoop, Python, Pyspark, Shell scripting, Linux, Jenkins, Eclipse, Oracle, Git, Tableau, MySQL Client: Solventum, Saint Paul, MN Jun 2021- Sep 2023 Role: Python Developer Responsibilities: Responsible for gathering requirements, system analysis, design, development, testing and deployment. Evaluated business requirements and prepared detailed specifications that follow project guidelines required to develop written programs. Exposed to various phases of Software Development Life Cycle using Agile Scrum Software development methodology. Fixed bugs, enhanced applications by improving code reuse and upgraded performance by making effective use of various design patterns. Developed end-to-end web applications combining Python (FastAPI) and .NET Core backends with Angular frontends for healthcare analytics solutions. Integrated LLM and RAG-based AI modules into Python APIs to automate document classification, summarization, and knowledge retrieval. Designed and implemented data-driven automation solutions leveraging Azure SQL, Azure Functions, and Key Vault for secure and scalable performance. Built and containerized Python/.NET microservices with Docker and deployed via Azure Kubernetes Service (AKS). Collaborated with solution architects to define and document data flow and API integration patterns across distributed systems. Implemented CI/CD pipelines using Azure DevOps for streamlined deployments and version-controlled code releases. Partnered with cross-functional teams to ensure compliance, security, and optimization of backend APIs and front-end workflows. Championed Agile best practices through sprint retrospectives, story refinement, and collaborative feature delivery. Built a distributed system for triggering and executing daily data processing jobs which contains a high-availability scheduler (built with Python), a cluster of workers (built with Python), and UI (built with Python and Django). Responsible for the development of entire backend modules using Python. Engineered a sales forecasting microservice in Python, training time series models with Prophet and deploying inference endpoints via Flask Designed feature engineering pipelines for customer segmentation, applying K means clustering in production to drive targeted analytics dashboards Partnered in cross functional teams to validate and deploy ML driven ETL transformations, ensuring data science experiments moved seamlessly into production Involved in deployment of projects using Amazon AWS. Wrote Lambda functions in python for AWS & Lambda which invokes python scripts to perform various transformations and analytics on large data sets. Validated the developed lambda scripts and fixed the identified bugs. Responsible for launching Amazon EC2 Cloud Instances and configuring the launched instances with respect to specific applications. Automating cloud infrastructure such as with cloud formation, ansible, etc. Developed Templates for AWS infrastructure as a code using Terraform to build staging and production environments. Monitoring the AWS Infrastructure Response Time, Application/Service availability, Backend Transaction time, Throughput using CloudWatch and Site24x7. Developed a fully automated continuous integration system using Git. Created tables, complex join queries, stored procedures, views, Types, triggers and Functions and modifications to existing database structure as required for addition of new features using SQL developer. Designed, developed, and implemented ETL pipelines using python API (pySpark) of Apache Spark on AWS EMR. Worked on performance tuning of PySpark scripts to ensure overall build delivery quality is good and on time delivery is always done. Scheduled the ETL jobs using Autosys. Worked on designing and implementing a fully operational production grade large scale data solution on Snowflake Data Warehouse. Developed and enhanced ETL s across various database products, product development research & testing, to drive improved client services, rapid, effective ingestion of data, and business growth. Using Bash Scripting, moved the files from one server to another server. Deleted or archived old log files using Bash scripting. Developed Unix Shell Scripts to send alert notifications when an error is identified. Used wrapper to run the Pentaho job to load large volumes of data. Environment: Python 3, Django, Docker, Kubernetes, Microservices, PostgreSQL, Subversion, Amazon Web Services (AWS), REST API, Linux, Shell Scripting, NumPy. Client: Hartford Insurance, Hartford, CT Dec 2019- May 2021 Role: Python Developer Responsibilities Created various Azure data bricks notebooks using Python for performing Data Transformations. Worked on SQL concepts like Aggregates, Views, Database objects, and stored procedures. Developed Python scripts to automate data extraction, transformation, and loading (ETL) from multiple data sources into MongoDB. Developed Spark applications using Pyspark and Spark-SQL for data extraction, transformation and aggregation from multiple file formats for analyzing & transforming the data to uncover insights into the customer usage patterns. Involved in transforming the data through Azure Data Bricks notebooks and adding them as the activity for data factory workflows. Created RESTful APIs in Python to enable data access and integration with other applications and services. Integrated data analytics solutions with BI (Business Intelligence) tools such as Tableau, Power BI, to enable self-service analytics and interactive data visualization for business users, executives, and stakeholders Designed and developed RESTful APIs in Python using FastAPI, enabling seamless data integration with external partners. Built and optimized data pipelines for ETL processes using Python and MongoDB, ensuring efficient data flow and storage. Maintaining version control of code using Azure Devops and GIT repository. Developed and designed system to collect data from multiple portal using Kafka and then process it using spark. Used to work with integration runtime to create linked services and to create data factory pipelines, data sets, and data flows and Azure Databrick. Wrote Python-based automation scripts for Kubernetes resource management, improving deployment efficiency by 40%. Involved in creating new reports and modifying existing reports like adding new filters, and parameters and changing the report logic using Power BI. Django & Azure Service Bus: Built Django consumers to process real time messages from Azure Service Bus, enabling event driven workflows for order processing with sub second latency. SQL/NoSQL Performance Tuning: Implemented advanced indexing strategies in PostgreSQL and sharding in MongoDB, resulting in a 3 reduction in average query response times under peak load. Flask Webhooks & Security: Engineered Flask based webhook endpoints to securely ingest payloads from third party payment gateways, incorporating HMAC validation to meet PCI DSS standards. Environment: Microsoft Azure Services, Visual Studio, Azure DevOps, Azure SQL Data Warehouse, Power BI, Azure Databricks, EventHub, Azure Data Factory, Azure synapse analytics Client: T-Mobile, Seattle, WA Jan 2017 Nov 2019 Role: Python Developer Responsibilities: Involved in analysis, specification, design, and implementation and testing phases of Software Development Life Cycle (SDLC) and used agile methodology for developing application. Participate in requirement gathering and analysis phase of the project in documenting the business requirements by conducting workshops/meetings with various business users Worked on Python Open stack API and used Python scripts to update content in the database and manipulate Files Deployed machine learning solutions in Python to classify millions of previously unclassified Twitter users into core data product. Performed Data mapping between source systems to Target systems, logical data modeling, created class diagrams and ER diagrams and used SQL queries to filter data. Used various techniques using R data structures to get the data in right format to be analyzed which is later used by other internal applications to calculate the thresholds. Deployed the ETL and REST services on AWS ECS through the CI/CD Jenkins pipe. Generated Python Django forms to record data of online users and used PyTest for writing test cases. Worked on optimizing and memory management of the ETL services. Wrote and executed various MYSQL database queries from Python using Python-MySQL connector and MySQL db package. Implemented Okta Single Sign on Authentication for several applications making using of Python SDKs, SAML and OpenID Connect. Used PySpark-SQL to load JSON data and create schema RDD, Data Frames and loaded it into Hive Tables and handled Structured data using Spark-SQL. Utilized Python Libraries like Boto3, NumPy for AWS. Used Pandas library for statistical Analysis Used Python and Django to interface with the jQuery UI and manage the storage and deletion of content. Created a full web stack using AWS Infrastructure (Beanstalk, multiple lambdas, Amazon Aurora, API Gateway etc.) to create a fully functioning API using GraphQL technology with multiple data sources. Implemented machine learning schemes using Python libraries scikit-learn and SciPy. Implemented PySpark using Python and utilizing data frames and temporary table SQL for faster processing of data. Worked on several python packages like Matplotlib, Pillow, NumPy, SQL Alchemy, and sockets. Implemented AWS high availability using AWS Elastic Load Balancing (ELB), which performed balance across instances. Involved in development of Web Services using SOAP for sending and getting data from the external interface in the XML format. Environment: Python 3, libraries - (NumPy, SciPy, Pandas, SNMP, PyCharm, PyQuery, Matplotlib), SQL Alchemy, MVC, Linux Suse, OAuth 2.0, Slack, OIDC, Kubernetes, Kafka, Shell Scripting, JSON, Apache Web Server, SQL, UNIX. Client: Cyient, India Aug 2014- Mar 2016 Role: Data Engineer Responsibilities: Worked as Data Engineer to review business requirement and compose source to target data mapping documents. Conducted technical orientation sessions using documentation and training materials. Gathered the business requirements from the Business Partners and Subject Matter Experts. Served as technical expert guiding choices to implement analytical and reporting solutions for client. Designed and deployed Azure Data Lake Storage and Azure Data Factory for batch and real-time data processing, ETL workflows, and data integration across heterogeneous data sources. Implemented Azure SQL Database and Azure Cosmos DB for structured and unstructured data storage, optimizing database performance and scalability. Developed data pipelines using Azure Functions and Logic Apps for event-driven data processing and automation of data workflows. Worked closely with the business, other architecture team members and global project teams to understand, document and design data warehouse processes and needs. Implemented Installation and con guration of multi-node cluster on Cloud using Amazon Web Services (AWS) on EC2. Developed reconciliation process to make sure elastic search index document count match to source records. Maintained Tableau functional reports based on user requirements. Created action lters, parameters, and calculated sets for preparing dashboards and worksheets in Tableau. Used Agile (SCRUM) methodologies for Software Development. Developed data pipelines to consume data from Enterprise Data Lake (MapR Hadoop distribution Hive tables/HDFS) for analytics solution. Created Hive External tables to stage data and then move the data from Staging to main tables. Wrote complex Hive queries to extract data from heterogeneous sources (Data Lake) and persist the data into HDFS. Implemented the Big Data solution using Hadoop, hive and Informatica to pull/load the data into the HDFS system. Developed incremental and complete load Python processes to ingest data into Elastic Search from Hive. Pulled the data from data lake (HDFS) and massaging the data with various RDD transformations. Created Oozie work ow and Coordinator jobs to kick o the jobs on time for data availability. Developed Rest services to write data into Elastic Search index using Python Flask speci cations Developed complete end to end Big-data processing in Hadoop eco system. Used AWS Cloud with Infrastructure Provisioning / Con guration. Used Hive to analyze the partitioned and bucketed data and compute various metrics for reporting on the dashboard. Created dashboards for analyzing POS data using Tableau. Developed Tableau visualizations and dashboards using Tableau Desktop. Involved in PL/SQL query optimization to reduce the overall run time of stored procedures. Used Hive to analyze the partitioned and bucketed data and compute various metrics for reporting on the dashboard. Implemented partitioning, dynamic partitions and buckets in Hive. Deployed RMAN to automate backup and maintaining scripts in recovery catalog. Environment: AWS, Python, Agile, Hive, Oracle 12c, Tableau, HDFS, PL/SQL, Sqoop, Flume Keywords: csharp continuous integration continuous deployment artificial intelligence machine learning user interface javascript business intelligence sthree database active directory rlang information technology procedural language Connecticut Minnesota Texas Washington