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Meghana Sharma - Data Engineer

[email protected]

Location: Irving, Texas, USA

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Visa: H1B

Resume file: Meghana G. Sharma (Lead Data Engineer, DP-203 Certified)_1777836705476.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

MEGHANA G. SHARMA Lead Data Engineer | DP-203 Certified (609) 643 5166 ~ Open for anywhere in US ~ CTC Only SUMMARY OF QUALIFICATIONS: o Results-driven IT Professional with 12+ years of progressive experience delivering enterprise-scale data engineering, analytics, and modernization solutions across telecommunications, financial services, retail banking, and government domains. o Proven expertise in designing and implementing cloud-native data platforms using AWS and Microsoft Azure, with deep hands-on experience in Azure Databricks, Azure Data Factory, AWS Glue, Apache Airflow, Snowflake, and Lakehouse architecture using Medallion (Bronze/Silver/Gold) patterns leveraging Azure Data Lake Storage and Delta Lake. o Extensive experience in data modeling and transformation, including dimensional modeling (fact/dimension tables), Slowly Changing Dimensions (SCD Type 1 & 2), and implementation of data quality, validation, and reconciliation frameworks. o Strong focus on performance optimization, with hands-on experience tuning Spark jobs, SQL queries, indexing strategies, and batch workflows to support high-volume data processing and reporting workloads. o Strong background in enterprise data modernization, including legacy-to-cloud migrations, core financial system onboarding, and building scalable ETL/ELT pipelines for analytics-ready data delivery. o Demonstrated success implementing CI/CD and DevOps practices using Azure DevOps, Git, and automation frameworks to enable controlled, repeatable deployments across Development, QA, and Production environments. o Deep foundation in enterprise database engineering, including Oracle and Azure SQL platforms, with expertise in schema design, stored procedures, batch processing, and transactional data systems supporting mission-critical applications. o Experience delivering solutions in highly regulated environments, including banking and government systems, with strong emphasis on data accuracy, auditability, security, and compliance. o Proven technical leadership and collaboration skills, having led sprint planning, code reviews, mentoring, and cross-functional coordination with architects, DBAs, QA teams, and business stakeholders in Agile delivery models. AWARDS & HONORS, CERTIFICATIONS & EDUCATION: o Infosys Best Employee of the Month July 2016 o Infosys Award of Rewards and Recognition in Green Horn Category o Infosys INSTA Award o Spot Award at EVRY o Cognizant P&R Awards in Bronze Category o Microsoft Certified: Azure Data Engineer Associate (DP-203) o Microsoft Certified: Azure Solutions Architect (AZ-303, AZ-304) o Bachelor s Degree in Information Science (2012 Graduate) / Siddhartha Institute of Technology (VTU) - Karnataka, India PROFESSIONAL EXPERIENCE: Client: Capital One Dallas, TX Oct 2024 Present Role: Lead Data Engineer, Azure Engineer This project focused on building and enhancing a secure, scalable Azure-based data engineering platform to support enterprise analytics, reporting, and data-driven decision-making initiatives. The scope involved ingesting, transforming, and curating large volumes of structured financial data from multiple enterprise systems into cloud-native analytical data stores. The project emphasized modern data engineering practices, including cloud-native orchestration, distributed data processing, performance optimization, and data quality enforcement using Azure Data Factory, Azure Databricks, and Azure Data Lake Storage. The engagement supported analytics use cases such as customer segmentation, pricing analysis, and operational reporting, while ensuring secure, reliable, and production-ready data pipelines aligned with Capital One s enterprise data standards. o Designed and built scalable Azure data pipelines using ADF and Databricks to ingest and process multi-source enterprise data o Architected and implemented scalable ELT workflows using Azure services (including Azure Data Factory, Azure Data Lake Storage Gen2, and Azure Databricks, Azure Synapse, Azure Key Vault, Azure Logic Apps) leveraging cloud-native processing patterns to standardize, integrate, and curate data across key banking domains such as Payments, Accounts, and Lending, with a primary focus on complex transaction datasets. o Implemented distributed data transformations using Azure Databricks (PySpark), processing high-volume datasets with optimized Spark jobs for performance and scalability. o Implemented incremental data processing and change handling patterns to efficiently manage data refreshes and reduce processing overhead. o Tuned data pipelines and Spark jobs using partitioning strategies, caching and Improved pipeline performance by 40% o Developed and optimized dimensional data models (fact and dimension tables) to support analytical queries and BI consumption. o Applied data validation, reconciliation, and quality checks using Python and SQL to ensure data accuracy, consistency, and reliability across pipelines. o Implemented version control and release management practices using Git and Azure DevOps, supporting controlled deployments and collaborative development. Environment: Azure Data Factory (ADF v2), Azure Databricks (PySpark), Azure Data Lake Storage Gen2, Azure SQL Database, SQL, Python, Azure DevOps, Git, Power BI, Agile/Scrum Employer: Cognizant Bengaluru, India Feb 2022 Feb 2024 Role: Team Lead Data Engineer Project: Merch Insight The Merch Insight project focused on building a centralized data platform to support merchandise planning, ordering, and inventory optimization across multiple regions, seasons, and store clusters. The platform enabled business planners to create and manage long-term merchandise plans by leveraging historical sales data, seasonal trends, and store-level attributes.The project involved designing and implementing scalable AWS-based data pipelines to ingest, transform, and consolidate data from multiple operational systems into an analytical data store, supporting planning workflows, approval processes, and reporting needs. The solution provided improved visibility into inventory, demand forecasts, and approval statuses, enabling stakeholders to make informed, data-driven decisions and improve overall merchandise planning and execution. o Designed and implemented ELT pipelines using AWS Glue (PySpark) to ingest structured and semi-structured data from APIs o Orchestrated workflows using Apache Airflow DAGs integrated with AWS services (S3, Glue), enabling reliable scheduling, dependency management, monitoring, and automated recovery of data pipelines. o Engineered Glue jobs to extract data from SQL Server and internal retail systems, applying PySpark transformations and delivering datasets to external partners via AWS Lambda + SFTP integrations o Designed dimensional data models (fact and dimension tables) supporting analytics such as customer behavior, order trends, and inventory tracking o Optimized Snowflake query performance using clustering and warehouse tuning, reducing query latency by 35% and lowering compute costs o Built feature-ready datasets for ML use cases such as demand forecasting and customer segmentation o Participated across the full data lifecycle in an Agile environment, collaborating with cross-functional teams Environment: AWS (S3, AWS Glue, IAM), Snowflake (Cloud Data Warehouse), SQL, Python, ELT Pipelines, Cloud-Native Data Architecture, Enterprise Data Modeling (Facts & Dimensions), Data Validation & Reconciliation, Performance Tuning & Optimization, Data Quality Checks, Enterprise Business Model (EBM), Agile / Scrum, Version Control (Git), CI/CD Client: Oslo Banks Norway, Oslo (Onsite) Dec 2017 Feb 2022 Employer: EVRY India Pvt. Ltd Bengaluru, India Role: Lead Data Engineer The Retail Banking Services project for Oslo-based banks focused on building and enhancing a centralized data and transaction platform to support core retail banking operations in compliance with regional regulatory standards. The platform enabled secure management of customer accounts, retail banking products, and high-volume financial transactions across multiple channels. Key functional areas included account lifecycle management, transaction processing, interest calculation, statement generation, and loan and credit monitoring. The project emphasized data accuracy, performance, and reliability, with strong controls around validation, auditability, and risk management. As part of the engagement, the team designed and optimized enterprise-grade database solutions and supported early-stage cloud adoption initiatives, ensuring scalable, secure, and compliant data handling for mission-critical banking workloads. o Served as Lead Data Engineer, overseeing database design, development, and performance optimization for core retail banking data platforms. o Collaborated closely with clients, product owners, and business stakeholders to understand regulatory, functional, and reporting requirements, translating them into optimized data and database designs. o Designed and implemented conceptual, logical, and physical data models to support customer accounts, transactions, loans, and retail banking products. o Developed and optimized database packages, stored procedures, and functions to support transaction processing, interest calculations, statement generation, and batch workloads. o Implemented database indexing and partitioning strategies to improve query performance and ensure scalability for high-volume transactional data. o Performed query performance tuning on SQL generated by Java-based applications (Hibernate / jOOQ), resolving bottlenecks and improving response times. o Built and executed unit test cases for database objects to ensure correctness, reliability, and regression safety. o Supported batch processing workflows for periodic account updates, interest computations, and reporting processes. o Conducted code reviews and provided technical guidance to team members, ensuring adherence to best practices in database development and performance optimization. o Led knowledge transfer and mentoring sessions for team members from diverse technical backgrounds, improving overall team capability and delivery quality. o Utilized CI/CD and version control tools to manage database changes, deployments, and releases in a controlled and auditable manner. o Contributed to the setup and management of Azure resources in support of application hosting and supporting services, aligning with early cloud adoption initiatives during the project timeline. Environment: Oracle Database 12c, Oracle SQL Developer, SQL/PLSQL, Database Design & Modeling, Indexing & Partitioning, Flyway, Jenkins, Docker, Azure (basic resource usage), JIRA, Visual Studio, Tortoise SVN Client: Income Tax Department Govt. of India Jan 2015 Dec 2017 Employer: Infosys Bengaluru, India Role: Sr. Database Engineer The Income Tax Returns (ITR) Processing System was a large-scale, mission-critical tax administration platform developed for the Government of India to automate the end-to-end processing of electronically filed income tax returns across assessment years. The system handled high-volume taxpayer data and executed complex, rule-driven validations in accordance with the Income Tax Act and annual Finance Act amendments. The scope included initial data validations, tax re-computation, interest and penalty calculations, refund or demand determination, and generation of statutory intimations and notices. The platform also supported grievance handling, rectification workflows, audit trails, and SLA-driven issue resolution, ensuring secure, accurate, and transparent tax processing during peak filing periods with strict compliance and availability requirements. o Analyzed business requirements, statutory rules, and technical specifications to design and implement database solutions aligned with government tax regulations. o Designed, developed, and maintained Oracle database schemas and objects, including tables, views, materialized views, indexes, constraints, triggers, sequences, and synonyms to support large-scale tax processing workloads. o Developed and optimized PL/SQL packages, procedures, and functions to implement rule-based validations, tax calculations, interest computation, refunds, and demand determination logic. o Implemented implicit and explicit cursors, cursor loops, and reference cursors to efficiently process large datasets and complex transactional workflows. o Performed unit testing, debugging, and validation of database code to ensure accuracy, stability, and compliance with statutory requirements. o Supported system integration testing (SIT) and assisted with deployment of database changes into production environments. o Optimized database performance through indexing strategies, query tuning, and execution plan analysis, ensuring reliable performance during high-volume peak filing cycles. o Implemented and managed Oracle Job Scheduler processes to support batch executions, periodic validations, and scheduled processing activities. o Conducted peer code reviews to enforce coding standards, performance best practices, and data integrity. o Collaborated with business users and stakeholders to clarify requirements, assess impact of changes, and obtain approvals through detailed test results and documentation. o Produced and maintained technical design documents and project artifacts, ensuring traceability of changes and adherence to project standards. o Supported production management teams by investigating and resolving database-related incidents and data issues within defined SLAs. o Delivered knowledge transfer sessions to new and existing team members, ensuring continuity and operational readiness. Environment: Oracle Database, Oracle SQL, PL/SQL, Oracle SQL Developer, Oracle Job Scheduler, Database Design & Modeling, Indexing & Performance Tuning, Tortoise SVN, Notepad++, Enterprise Production Support Client: Deutsche Bank New York, NY June 2013 Jan 2015 Employer: TCS Bengaluru, India Project: Eagle Pace Role: Software Engineer Eagle PACE is a centralized Reference Data Management (RDM) platform used by the Data Services group within Deutsche Asset Management to support trading, accounting, and fund management systems. The platform acted as a Golden Data Repository, ingesting securities reference data from multiple external vendor sources across different subject areas. Incoming data was validated, standardized, and enriched using predefined business rules to create a trusted golden copy of security master data. The system supported creation of new securities, ongoing data refreshes from vendor feeds, and outbound data distribution to downstream trading and accounting applications. The platform played a critical role in ensuring data accuracy, consistency, and timeliness across enterprise investment systems. o Contributed to the design and development of reference data processing modules supporting security master data ingestion, validation, and enrichment workflows. o Developed and enhanced Oracle PL/SQL packages, procedures, and functions to implement business rules for data validation, transformation, and golden record creation. o Supported ingestion of reference data from multiple vendor feeds, applying rule-based checks to ensure data accuracy, completeness, and consistency before downstream distribution. o Implemented and optimized database objects including tables, indexes, views, and constraints to support high-volume reference data processing. o Proactively tuned stored procedures and SQL queries to improve system performance and reduce batch processing times. o Designed and maintained outbound data feeds supplying validated reference data to trading, accounting, and fund management systems. o Assisted in new security creation workflows, enabling timely availability of reference data for downstream trading and accounting applications. o Worked closely with client stakeholders and business users to understand data requirements, clarify rules, and support implementation and change requests. o Supported batch scheduling and monitoring using Control-M to ensure timely execution of reference data loads and refresh processes. o Handled multiple functional modules simultaneously, providing operational support and resolving data and processing issues. o Participated in code reviews and knowledge sharing, ensuring adherence to development standards and best practices. Environment: Oracle Database, Oracle SQL, PL/SQL, Oracle SQL Developer, Control-M Job Scheduler, Database Design & Performance Tuning, Tortoise SVN, Notepad++, Enterprise Banking Systems Employer: TCS Bengaluru, India Dec 2012 May 2013 Role: Software Engineer Project: Hospital Management The Hospital Management System project involved the development and enhancement of an enterprise application designed to manage core administrative and operational functions of a hospital. The application was structured into multiple functional modules, including building management, department management, ward and bed allocation, and billing. The system aimed to streamline hospital operations by centralizing data, improving resource utilization, and supporting day-to-day administrative workflows. As part of this early-career engagement, the role provided hands-on exposure to application development, database interactions, and testing activities within a structured enterprise delivery environment. o Contributed to the development of a new sub-module for Building Management, supporting configuration and management of hospital infrastructure data. o Assisted in enhancing existing application modules to accommodate evolving business and functional requirements. o Developed and maintained database components using Oracle SQL and PL/SQL to support application functionality and data persistence. o Participated in the preparation of business scenarios, test cases, and user manuals, supporting application validation and user adoption. o Gained hands-on experience in functional testing, user acceptance testing (UAT), and defect analysis, ensuring application quality and stability. o Collaborated with senior developers and functional teams to understand requirements, troubleshoot issues, and support application enhancements. Environment: Oracle Database, Oracle SQL, PL/SQL, Oracle SQL Developer, C++, Visual Studio, Functional & UAT Testing, Enterprise Application Development CORE SKILLS: Cloud Platforms & Data Services: Microsoft Azure, Azure Data Factory (ADF v2), Azure Databricks, Azure Data Lake Storage (ADLS Gen2), Azure Synapse Analytics, Azure SQL Database, Azure Blob Storage, Azure Key Vault, Azure Virtual Machines, Azure Logic Apps, AWS S3, AWS Glue, IAM, AWS EC2 Data Engineering & Big Data: End-to-End Data Pipeline Design, ELT / ETL Architecture, Distributed Data Processing, Batch Data Processing, Incremental Data Loads, Data Quality & Validation Frameworks, Lakehouse Architecture, Delta Lake (ACID Transactions, Upserts, Schema Enforcement), Dimensional Data Modeling (Fact & Dimension Tables), Slowly Changing Dimensions (SCD Type 1 & Type 2), Medallion Architecture (Bronze/Silver/Gold), CDC (Change Data Capture) Programming & Query Languages: Python, PySpark, SQL, T-SQL, PL/SQL Databases & Warehousing: Oracle Database (12c), Azure SQL Database, AWS RDS, SQL Server, Snowflake (Data Loading & Query Optimization where applicable), Database Schema Design, Indexing & Partitioning, Stored Procedures, Functions, Packages, Query Optimization & Performance Tuning Orchestration & Automation: Azure Data Factory Pipelines & Triggers, Control-M Job Scheduler, Oracle Job Scheduler, Batch Processing Frameworks CI/CD, DevOps & Version Control: Azure DevOps (CI/CD Pipelines), GIT, Jenkins, Flyway, Docker, TFS, Tortoise SVN Monitoring, Security & Governance: Pipeline Monitoring & Logging, Failure Handling & Alerts, Secure Secrets Management (Azure Key Vault), Role-Based Access Control (RBAC), Data Validation & Audit Controls Reporting & BI: Power BI, Analytical Query Optimization, BI Data Preparation & Consumption Development Tools: Azure Data Studio, Oracle SQL Developer, SQL Server Management Studio (SSMS), Jupyter Notebook, Visual Studio, Notepad++ Methodologies & Collaboration: Agile / Scrum, Sprint Planning & Backlog Grooming, Requirement Analysis & Stakeholder Collaboration, Code Reviews & Technical Mentoring, Production Support & Incident Resolution REFERENCES: Provided upon request Keywords: cplusplus continuous integration continuous deployment quality analyst machine learning business intelligence sthree rlang information technology procedural language Arizona New York Texas