Dharani Medapati
Fairborn
Summary
Data Engineer with around 2 years of experience in building and optimizing scalable data pipelines and solutions in cloud environments. Skilled in AWS services such as Redshift, S3, Glue, and Lambda, and proficient in SQL, Python, and Spark for ETL processes. Strong background in data modeling, performance tuning, and delivering actionable insights to support business decisions. Passionate about automation, data integrity, and leveraging new technologies to solve complex data challenges in fast-paced environments.
Overview
2
2
years of professional experience
1
1
Certification
Work History
Data Engineer
Metiz Technologies LLC
09.2023 - Current
- Designed, developed, and maintained scalable ETL pipelines for batch and real-time data ingestion using AWS Glue, Lambda, and Kinesis.
- Built robust data models and data warehouse schemas in Amazon Redshift, supporting analytical reporting, dashboards, and machine learning use cases.
- Developed automated workflows with PySpark and SQL, optimizing data transformation and improving processing time by up to 40%.
- Implemented data quality checks, monitoring, and logging using CloudWatch, Athena, and AWS CloudTrail to ensure data integrity and traceability.
- Tuned SQL queries and Spark jobs to improve performance and reduce cloud costs; used profiling tools for identifying performance bottlenecks.
- Created and managed data lakes using S3, structuring data in Parquet/ORC formats to support efficient querying and storage.
- Leveraged non-relational databases (e.g., DynamoDB, S3) for semi-structured and unstructured data storage and analysis.
- Built data access layers and APIs for secure, scalable data delivery to downstream consumers using IAM roles, API Gateway, and Lambda.
- Integrated DevOps practices into data workflows using Git, CI/CD pipelines, and Terraform for reproducible infrastructure deployment.
- Collaborated with data scientists, analysts, and stakeholders to translate business requirements into technical specifications and data products.
- Stayed up to date with big data technologies such as Spark, Kafka, Hive, and EMR, and led PoCs to evaluate new tools for scalability and cost optimization.
- Followed agile methodologies and participated in daily standups, sprint planning, and retrospectives to ensure timely and quality delivery.
Education
Master of Science - Data Analytics
Dakota State University
Madison, SD08-2023
Skills
- Amazon Web Services (AWS):
S3, Redshift, Glue, EMR, Lambda, EC2, RDS, Kinesis (Data Streams, Firehose, Analytics), Athena, IAM, CloudWatch - Apache Airflow, AWS Glue, Azure Data Factory, Informatica
- PySpark, Spark SQL, Kafka (for streaming ETL)
- Python, Scala, Java, SQL, Shell Scripting
- Amazon Redshift, Snowflake, PostgreSQL, MySQL, SQL Server, DynamoDB, MongoDB
- Star & Snowflake Schema Design, Dimensional Modeling
- Tableau, Power BI, QuickSight
- Data Lakes on AWS S3 or Azure Data Lake
- AWS IAM, VPC, KMS, Key Vault
- Role-based Access Control (RBAC), Encryption at Rest & Transit
- Git, Jenkins, Docker, Terraform, CloudFormation
- CodePipeline, CodeBuild, CodeDeploy
- Unit Testing (pytest, unittest), Data Validation Tools (Great Expectations)
- Monitoring: CloudWatch, ELK Stack, Datadog
- Git, GitHub, GitLab, Bitbucket
- JIRA, Confluence, Agile/Scrum methodologies
Certification
- AWS Certified Data Engineer
- Microsoft Azure data associate
Timeline
Data Engineer
Metiz Technologies LLC
09.2023 - Current
Master of Science - Data Analytics
Dakota State University
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