Data Engineer
Resume:
Ramkiran Akula
Data Engineer – AWS & Azure
************@*****.***
United States
Professional Summary:
Data Engineer with 5–6 years of experience in designing, building, and optimizing scalable data pipelines
Strong expertise in developing end-to-end ETL/ELT solutions for data integration and workflow automation
Experienced in building cloud-based data platforms within AWS ecosystems
Proficient in handling structured and unstructured data from multiple sources with high reliability and low latency
Hands-on experience with AWS services including S3, Glue, Lambda, and Redshift
Skilled in building and optimizing data pipelines for performance, scalability, and cost efficiency
Developed and maintained ETL/ELT workflows using Apache Airflow and AWS Glue
Improved pipeline orchestration, scheduling, and monitoring processes
Designed and implemented data lakes and data warehouse solutions
Strong knowledge of data modeling, schema design, and partitioning strategies
Experience with big data processing tools such as PySpark and Apache Spark
Built both real-time and batch data processing pipelines
Enabled near real-time analytics and business intelligence reporting
Implemented data quality checks and validation frameworks using Python
Ensured data accuracy, completeness, and consistency across pipelines
Worked with CI/CD tools like GitHub Actions and Terraform for automation
Improved deployment processes and reduced manual intervention
Contributed to cloud migration and modernization initiatives
Transitioned legacy systems to scalable cloud architectures
Collaborated with data analysts, data scientists, and business stakeholders
Delivered data solutions aligned with business requirements
Familiar with data governance, security, and access control best practices
Ensured secure handling of sensitive data and compliance standards
Strong troubleshooting and performance tuning skills
Improved pipeline efficiency and reduced processing time
Adaptable to fast-paced environments and capable of managing multiple priorities
Consistently delivered reliable and high-quality data solutions
Technical Skills:
Cloud Platforms AWS (Amazon Web Services), GCP (Google Cloud Platform), Microsoft Azure
Data Processing & Computing PySpark, Apache Spark, AWS EMR, Databricks, AWS Lambda
Data Warehousing & Query Engines Amazon Redshift, Snowflake, Google BigQuery
Data Orchestration & Workflow Apache Airflow, AWS Step Functions, AWS Glue Workflows
Database & Stream Processing MySQL, Amazon Kinesis, Apache Kafka Data Storage & Data Lakes AWS S3, AWS Lake Formation
ETL/ELT Tools AWS Glue
Data Quality & Preparation Great Expectations, AWS DataBrew
MLOps & AI/ML AWS SageMaker, MLOps, LLMs (Large Language Models), GenAI, Feature Engineering
Infrastructure as Code (IaC) Terraform, AWS CloudFormation, Azure DevOps CI/CD & DevOps AWS CodePipeline, GitHub Actions, Docker
Data Governance & Cataloging AWS Glue Catalog, Apache Atlas, Collibra
Monitoring & Visualization AWS CloudWatch, AWS Cost Explorer, AWS QuickSight
Security & Compliance AWS IAM, Encryption, Audit Logging, SOC2, HIPAA, GDPR
Programming & Scripting Python, Bash, Power Shell
Work Experience:
Data Engineer CVS Health Jun 2023 – Present
Responsibilities:
Designed and developed scalable data pipelines using AWS Glue, AWS Lambda, and Amazon S3, enabling efficient ingestion, transformation, and integration of structured and semi-structured data across multiple systems with improved latency.
Built and maintained ETL/ELT workflows using AWS Glue, PySpark, and Amazon Redshift, supporting data transformation, scheduling, and performance optimization for analytical workloads.
Contributed to the development and enhancement of data lake architectures on Amazon S3, implementing access controls, data partitioning, and lifecycle policies for efficient and cost-effective storage.
Collaborated with data scientists and analysts to support data-driven and ML-based use cases, preparing and transforming datasets for forecasting, reporting, and business insights.
Assisted in implementing ML workflow automation using AWS tools such as SageMaker and Step Functions, supporting model deployment and monitoring processes.
Developed and optimized queries and data models using Amazon Redshift, PostgreSQL, and DynamoDB, improving data retrieval performance and supporting reporting requirements.
Implemented data quality validation checks using tools like Great Expectations and custom Python scripts to ensure data consistency, completeness, and reliability.
Built batch and near real-time data pipelines using Amazon Kinesis, Kafka (or similar tools), and AWS Lambda to support event-driven data processing.
Utilized Python and PySpark for data transformation, pipeline development, and performance tuning for large-scale datasets.
Supported CI/CD processes using tools such as GitHub Actions, AWS CodePipeline, and Terraform to automate deployment and improve development workflows.
Maintained metadata and data cataloging using AWS Glue Catalog, supporting data discovery, lineage tracking, and governance initiatives.
Worked closely with cross-functional teams to define data models, KPIs, and reporting requirements, translating business needs into scalable data solutions.
Monitored and optimized AWS resources using CloudWatch and Cost Explorer, contributing to cost reduction and efficient resource utilization.
Applied DataOps best practices to improve pipeline reliability, collaboration, and deployment efficiency.
Ensured adherence to data security and governance standards using AWS IAM roles, encryption, and access control mechanisms.
Orchestrated workflows using Apache Airflow and AWS Step Functions, improving job scheduling, monitoring, and error handling.
Provided guidance and knowledge sharing to junior team members, contributing to team productivity and adoption of best practices.
Data Engineer Care Source Insurance May 2022 – Jun 2023
Responsibilities:
Designed and implemented data integration pipelines using Azure Data Factory, orchestrating workflows between on-premises and cloud systems to enable scalable and automated data movement.
Developed and maintained ETL pipelines using Azure Databricks and PySpark, performing data transformation, cleansing, and enrichment across multiple business datasets.
Worked with Azure Data Lake Storage (ADLS Gen1/Gen2) to store and manage structured and unstructured data, applying partitioning and lifecycle strategies for optimized performance and cost efficiency.
Built data ingestion pipelines into Azure SQL Database and Azure Data Lake, improving data accessibility and supporting reporting and analytics use cases.
Developed and optimized data models using Azure SQL and SSIS, enhancing query performance and supporting high- volume analytical workloads.
Created and maintained data orchestration workflows using Azure Data Factory and Logic Apps, automating data flows and reducing manual processing efforts.
Implemented near real-time data processing solutions using Azure Event Hubs, enabling ingestion of streaming data for operational insights.
Performed SQL performance tuning and query optimization using T-SQL, indexing, and partitioning techniques to improve execution efficiency.
Implemented data validation and quality checks using Python and Azure Functions, ensuring reliability and accuracy of data pipelines.
Supported data security and access control using Azure Active Directory (AAD), ensuring proper role-based access and protection of sensitive data.
Contributed to the development of data warehouse solutions using Azure Synapse Analytics (formerly SQL Data Warehouse) for enterprise reporting and analytics.
Collaborated with data architects, analysts, and business stakeholders to define data models, mappings, and business rules, translating requirements into scalable solutions.
Monitored pipelines using Azure Monitor and Log Analytics, identifying and resolving performance bottlenecks and failures.
Developed Power BI dashboards and reporting layers, enabling business users to visualize KPIs and make data-driven decisions.
Supported CI/CD processes using Azure DevOps, automating deployments and maintaining version control for data pipelines and scripts.
Troubleshot and resolved ETL and data pipeline issues, improving reliability and minimizing production downtime.
Assisted in mentoring junior team members and promoting best practices in Azure data engineering and code optimization.
Junior Data Engineer E-Kalasaala Jan 2020 – Dec 2021
Responsibilities:
Designed and developed ETL pipelines and data processing workflows using Amazon EMR (Hadoop/Spark), Python, and SQL, enabling efficient ingestion and transformation of large datasets from multiple sources.
Built and maintained data lakes on Amazon S3, applying partitioning, compression, and structured storage practices to improve performance and optimize storage costs.
Developed and optimized data transformation workflows using PySpark, Python, and Shell scripting, ensuring reliable and scalable processing for analytical and reporting use cases.
Managed and supported Amazon Redshift environments, creating optimized schemas, sort keys, and distribution strategies to improve query performance and data access.
Implemented near real-time data ingestion pipelines using Amazon Kinesis and AWS Lambda, supporting event- driven processing and timely business insights.
Collaborated with data analysts, data scientists, and BI teams to design data models and curated datasets for reporting, dashboards, and analytics use cases.
Created and scheduled data ingestion and processing jobs using cron, Apache Oozie, and custom scripts, ensuring consistent and timely data availability.
Applied data security and governance practices using AWS IAM roles, encryption, and access control mechanisms to protect sensitive data.
Performed SQL performance tuning and optimization across Amazon Redshift, MySQL (RDS), and PostgreSQL to improve query efficiency and resource utilization.
Monitored pipeline performance and system health using Amazon CloudWatch and custom monitoring scripts, proactively identifying and resolving issues.
Developed data validation and quality checks using Python to ensure accuracy, consistency, and completeness across data pipelines.
Supported migration of legacy ETL workflows to AWS (EC2/EMR), improving scalability, reliability, and reducing operational overhead.
Worked with DevOps teams to implement infrastructure automation using AWS CloudFormation and scripting, enabling consistent environment setup and faster deployments.
Documented data pipelines, workflows, and dependencies through technical documentation and data flow diagrams, improving maintainability and team collaboration.
Stayed updated with AWS big data technologies and best practices, contributing to continuous improvements in data processing and pipeline efficiency.
Data Engineer Inter BHEL (Bharat Heavy Electricals Limited) Jun 2019 – Dec 2019 Responsibilities:
Assisted in the development of data-driven applications and internal platforms, improving data accessibility and usability for internal users and business processes.
Supported the design and implementation of data pipelines and backend systems using technologies such as Java (Spring), Oracle Database, and web frameworks, ensuring reliable data flow and storage.
Performed data extraction, transformation, and loading (ETL) tasks to integrate data from multiple sources into centralized databases for reporting and analysis.
Conducted basic data validation and quality checks, identifying inconsistencies and improving overall data accuracy and reliability.
Assisted in monitoring and troubleshooting data pipelines and application workflows, ensuring smooth data processing and minimal downtime during deployments.
Gained exposure to data security and system monitoring tools (e.g., network analysis and vulnerability scanning tools), contributing to improved data protection and system reliability.
Supported database management and query optimization using SQL, improving data retrieval performance for internal applications.
Collaborated with cross-functional teams to analyze data requirements and support reporting needs, translating business requirements into technical data solutions.
Assisted in implementing basic data governance and access control measures, ensuring secure handling of internal data.
Participated in deployment and testing of data-related features, ensuring data consistency, performance, and reliability in production environments.
Education:
MS, Computer Science and Information University of North Texas, TX (May 2024)
Bachelor of Technology, Electronics & Computer Science Engineering – (Jun 2021)
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