Data Engineer with Cloud & ETL Expertise

Naveen kukudala

Email: ****************@*****.***

Mobile: 732-***-****

LinkedIn: www.linkedin.com/in/naveen-kukudala-8a5568323 Data Engineer

PROFESSIONAL SUMMARY

Designed reliable data pipelines over three years, delivering curated datasets for banking, insurance and healthcare analytics across Azure, AWS and enterprise warehouse platforms. daily.

Developed batch and streaming ETL solutions with Python, SQL, Spark and Kafka, transforming complex source systems into governed models supporting reporting and regulatory stakeholders.

Implemented cloud data platforms on Azure, AWS and GCP components, standardizing ingestion, transformation and storage patterns to simplify downstream analytics and dashboard development. initiatives.

Engineered robust data quality checks, monitoring and alerting, increasing trust in financial, risk and healthcare metrics for cross-functional technology, operations and business teams enterprise-wide.

Mentored and guided teams to develop innovative solutions, resulting in a 30% increase in project efficiency.

Exhibited leadership skills by fostering an inclusive environment, which improved team morale and productivity.

Collaborated effectively across departments to streamline processes, enhancing interdepartmental communication by 25%.

TECHNICAL SKILLS

Cloud Platforms - AWS (EC2, Lambda, Glue, S3, Kinesis, IAM, EKS, Redshift), Azure (ADF, Synapse, Azure SQL, Entra ID, Key Vault), GCP (BigQuery, GKE, Cloud Storage), OpenShift

Infrastructure As Code (Iac) - Terraform, Ansible, ARM Templates, Bicep, CloudFormation, Jenkins, Azure DevOps

Monitoring And Incident Response - New Relic, AWS CloudWatch, Azure Monitor, ServiceNow, RCA, SLA Management, troubleshoot, resolve performance issues

Security And Compliance - IAM, Encryption, NIST 800-53, CIS Benchmarks, PCI-DSS, RBAC, Key Vault, Audit Logging

Ci/Cd And Devops - Jenkins, GitHub Actions, Git, GitLab, CodePipeline, CI/CD Pipelines, Shell Scripting Programming & Scripting - Python, SQL, Bash, PowerShell, automation pipeline management

Data Engineering - AWS Glue, Azure Data Factory, DBT, Apache Kafka, Spark, Hive, GCP Dataflow, ETL tools

Databases - Redshift, Snowflake, Azure SQL, PostgreSQL, MongoDB, MySQL, PL-SQL

Dashboards And Visualization - Power BI, Tableau, Looker, AWS QuickSight, Tableau Prep

Data Analytics Tools - Alteryx, RapidMiner

Software Architecture - large-scale architecture initiatives, enterprise rollouts

System Administration And Infrastructure - containers, containerized deployments PROFESSIONAL EXPERIENCE

Wells Fargo May 2025 – Present

Data Engineer

Optimized Azure Data Factory and Azure Databricks pipelines for Wells Fargo, reducing Spark processing windows and stabilizing delivery of regulatory, finance and risk datasets.

Streamlined ingestion of core banking systems into Azure Data Lake Storage and Synapse, organizing curated layers that simplified analytics, reconciliations and downstream consumption patterns.

Automated data quality validations with Python, SQL and Azure Data Factory, preventing schema drift and inaccurate balances from impacting regulatory and operational reporting consumers.

Integrated event-driven feeds and batch jobs across Azure Data Factory, Databricks and Kafka, ensuring availability of customer, product and reference data for dependent platforms.

Configured monitoring, logging and alerting across Azure orchestration components, improving visibility, shortening recovery times and strengthening reliability expectations for lending, deposits and card stakeholders.

Led data preparation and integration efforts, enhancing data accuracy by 25% and streamlining workflows for improved decision-making.

Developed workflow automation and architecture design, reducing manual processing time by 40% and increasing system reliability.

Implemented data processing automation and performance optimization, achieving a 30% increase in data throughput and reducing latency.

Enhanced code quality and scalability, supporting a 50% growth in user base without performance degradation.

Provided operational insights and ETL automation, driving a 35% improvement in data processing efficiency. Allied world Assurance Company April 2024 – August 2024 Data Engineer

Validated AWS data pipelines consolidating underwriting, claims and policy information into S3, Glue and Redshift, ensuring structures that supported actuarial, pricing and portfolio analytics.

Analyzed ETL workflows on EMR, Spark and Lambda, refactoring transformations and storage formats to Parquet to shorten processing windows for regulatory and management reporting.

Orchestrated Glue jobs, Redshift loads and refreshes with Airflow, coordinating dependencies that preserved data freshness for dashboards used by underwriting, finance and risk leadership.

Consolidated disparate insurance datasets into governed S3 zones, applying metadata and partitioning standards that significantly simplified data discovery, lineage understanding and self-service analytical exploration.

Enhanced AWS security controls by aligning S3 policies, Redshift permissions and encryption practices, protecting confidential insurance information while maintaining authorized analytical and operational access.

Exhibited leadership skills to mentor and guide teams, fostering a collaborative environment that increased project delivery speed by 20%.

Collaborated using PL-SQL and Alteryx to optimize data workflows, resulting in a 15% reduction in processing errors.

Utilized RapidMiner and Tableau Prep for advanced data analytics, leading to a 25% improvement in data visualization accuracy.

Managed OpenShift and ETL tools for seamless automation pipeline management, enhancing deployment efficiency by 30%.

Directed large-scale architecture initiatives and enterprise rollouts, achieving a 40% increase in system capacity. CGI January 2022 – August 2023

Junior Data Engineer

Monitored Azure Synapse and Databricks workloads for a CGI healthcare client, investigating performance metrics to proactively resolve bottlenecks affecting clinical, claims and operational reporting.

Refined healthcare data models in Azure SQL and Synapse, aligning patient, provider and encounter entities to deliver structures supporting quality, utilization and reimbursement analytics.

Coordinated ingestion from EHR, claims and ancillary systems into Azure Data Lake Storage, standardizing formats and business rules that improved reliability of healthcare dashboards.

Delivered healthcare analytics datasets using PySpark, SQL and Azure Data Factory, enabling analysts to investigate trends in admissions, readmissions, procedures and population health outcomes.

Established reusable Azure patterns for data quality checks, lineage documentation and deployment automation, reducing onboarding effort for new healthcare pipelines while strengthening compliance readiness.

Utilized containers and containerized deployments to streamline application delivery, reducing deployment times by 50%.

Troubleshot and resolved performance issues, ensuring 99.9% system uptime and enhancing user satisfaction.

Led scrum teams in enterprise-scale data initiatives, delivering projects 25% faster than previous timelines.

Designed enterprise-level data solutions, resulting in a 30% increase in data accessibility and user engagement.

Optimized task dependency tuning and scheduling, reducing job completion times by 20% and increasing operational efficiency.

EDUCATION

Master's in Information technology - Wilmington University

Bachelor's in Electronics and Communication Engineering - Anurag University

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