Meghana Tera

• Developed and maintained Java backend microservices to support Walmart’s e-commerce and data analytics platforms.
• Built robust ETL pipelines to extract and transform data from various internal and external sources.
• Used Apache Kafka for real-time data ingestion and stream processing. Indexed and searched structured/unstructured data using Elasticsearch and visualized insights with Kibana.
• Implemented data governance and quality checks using Python and SQL-based validations.
• Leveraged AWS services (S3, EC2, RDS, SNS, SQS) for scalable and fault-tolerant data processing.
• Modeled and optimized both logical and physical data structures to support analytics use cases.
• Collaborated with product and analytics teams to define data contracts and implement schema versioning.
• Developed RESTful APIs in Java to expose curated data sets to downstream services.
• Tuned Elasticsearch queries and index settings to reduce search latency by 40%.
• Contributed to DevOps pipelines with Jenkins and GitHub Actions for CI/CD automation.

• Built and maintained scalable data pipelines using PySpark, Python, and SQL on AWS EMR.
• Processed structured and semi-structured data using AWS Glue and stored outputs in S3 data lakes.
• Consumed and produced real-time data streams using Amazon MSK (Kafka) and Lambda integrations.
• Managed RESTful APIs and backend services using Java and deployed them on AWS EC2 and ECS.
• Implemented schema evolution and data versioning using AWS Glue Schema Registry .
• Designed event-driven workflows using AWS Step Functions, SNS, and SQS for loosely coupled microservices.
• Built CI/CD pipelines with GitHub Actions and deployed infrastructure using Terraform and AWS CloudFormation.
• Monitored and visualized system performance using CloudWatch, Elasticsearch (OpenSearch), and Kibana.
• Ensured data privacy, encryption, and access control using AWS IAM, KMS, and Lake Formation.
• Automated ETL workflows and job orchestration using Apache Airflow on MWAA (Managed Workflows for Apache Airflow).
• Used RDS and DynamoDB to manage transactional and NoSQL data workloads in backend systems.
• Collaborated with cross-functional teams in Agile environments to support DataOps and MLOps initiatives on AWS.

· Built and maintained ETL pipelines, facilitating efficient data extraction, transformation, and loading across multiple data sources.

· Utilized data modeling techniques to design and maintain databases, optimizing performance and data integrity.

· Managed data warehousing in AWS Redshift and S3, ensuring data accessibility and security through proper IAM role configuration and management.

· Leveraged AWS Glue and Lambda for automation of data pipelines and real-time data processing.

· Conducted queries using SQL, HiveQL, and Spark SQL for data analysis, reporting, and troubleshooting.

· Collaborated with cross-functional teams to define data requirements and optimize business intelligence strategies.

· Streamlined an ETL process, reducing data processing time by 30%.

· Migrated large-scale data workloads to AWS Redshift, leading to a 25% cost reduction in cloud storage.

● Designed and implemented a cloud-based data ingestion solution using Python, SQL, and Spark, improving data processing speed by 60%.

● Created ETL pipelines to extract, transform, and load data from multiple sources into a centralized data warehouse using AWS technologies such as S3, EC2, RDS, Glue, and EMR.

● Collaborated with cross-functional teams to define data requirements and optimize data ingestion workflows, enhancing data quality, accuracy, and consistency.

● Worked closely with clients to analyze business requirements and translated them into actionable data models and interactive Power BI reports.

● Processed and analyzed data from over 500 raw CSV and JSON files, ensuring the data was clean, accurate, and ready for use.

● Assisted senior analysts by crafting and executing SQL queries to extract critical insights from relational databases.

● Utilized advanced Excel functions, including VLOOKUP and Pivot Tables, to manage and analyze large datasets, supporting data-driven decision-making.

● Contributed to all phases of the Data Warehouse development lifecycle, from requirements gathering and testing to implementation, data migration, and ongoing project support.

● Designed and delivered interactive Power BI dashboards that allowed non-technical stakeholders to easily interpret complex datasets and make informed decisions.