Senior Data Engineer with Cloud Huge Data Experience

Mounika Nadendla

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

Mobile: 513-***-****

LinkedIn: www.linkedin.com/in/mounika-nadendla-04273819a Senior Data Engineer

PROFESSIONAL SUMMARY

Data Engineer with 4+ years of experience designing and delivering scalable data pipelines, ETL/ELT workflows, and cloud-native architectures across diverse industries.

Proficient in Azure (ADF, Databricks, Synapse, Event Hub) and AWS (Glue, Redshift, Lambda, Kinesis) ecosystems, with hands-on expertise in big data processing and optimization.

Skilled in developing data lakehouse solutions using Delta Lake and ADLS Gen2, enabling structured bronze– silver–gold architecture for governance, lineage, and high-performance analytics.

Strong background in building real-time streaming pipelines and event-driven architectures to support analytics, dashboards, and machine learning use cases.

Adept in SQL, Python, and PySpark for large-scale transformations, advanced partitioning, and performance tuning across enterprise data platforms.

Coordinated cross-functional team coordination and mentorship, fostering a cohesive work environment and skill growth.

Facilitated cross-departmental collaboration through excellent written oral communication skills, enhancing project efficiency.

Championed passion automation continual process improvement, leading to a 20% increase in operational productivity.

TECHNICAL SKILLS

Cloud And Data Platforms - AWS (S3, EC2, Redshift, Glue), GCP (BigQuery, Dataflow, GKE), OCI (ADW, GoldenGate, Data Integration), data infrastructure, Microsoft Fabric

Databases and Warehousing - Snowflake, Oracle, SQL Server, PostgreSQL, MySQL, MongoDB, Teradata, data architecture, query techniques, data artifacts, Oracle Exadata

Programming - Python, Scala, SQL (T-SQL, PL/SQL), Shell Scripting, algorithmic concepts, Perl

Big Data and ETL - Spark, Hadoop, Hive, HBase, MapReduce, Pig, Airflow, Talend, Informatica, SSIS, DataStage, Luigi, Prefect, Oozie, ETL patterns

Streaming - Kafka, Flink, Spark Streaming, Kinesis, GCP Pub/Sub, OCI Streaming, logging data

Visualization - Power BI, Tableau, QuickSight, QlikView, SSRS, Cognos, Excel, Seaborn, Plotly, data visualizations, data insights

Devops - Docker, Kubernetes, Jenkins, Git, Terraform, Service Level Agreements, Unity Catalog

Data Governance - Collibra, Oracle Data Catalog, Great Expectations HIPAA, GDPR, CCPA, ISO 27001, security model, privacy requirements, governance processes

System Administration - Linux-based processes, Unix file systems, Linux environment setup PROFESSIONAL EXPERIENCE

CVS Health May 2024 – Present

Senior Data Engineer

Designed and implemented scalable ETL/ELT pipelines on Google Cloud using Dataflow, Dataproc, and Cloud Composer (Airflow) to process structured and unstructured datasets from multiple sources.

Developed and optimized data warehouses and lakehouses using BigQuery and Cloud Storage, enabling high- performance analytics and reducing query costs by 30% through partitioning and clustering strategies.

Built real-time streaming pipelines leveraging Pub/Sub, Dataflow, and BigQuery to deliver low-latency insights for critical business applications and machine learning use cases.

Migrated legacy on-premises data systems to GCP-native architectures, ensuring improved scalability, reliability, and cost efficiency while applying Terraform and Deployment Manager for infrastructure automation.

Collaborated with data scientists and analysts to integrate Vertex AI, Looker, and BigQuery ML into production workflows, enabling advanced predictive modeling and self-service analytics.

Implemented robust data governance, monitoring, and security frameworks on GCP, ensuring compliance with organizational and regulatory requirements using IAM, Cloud Monitoring, and Data Catalog.

Orchestrated data flows using Data Warehousing and orchestration tools, enhancing system scalability and reducing data processing time by 40%, resulting in improved data-driven decision-making capabilities.

Engineered backend focus solutions on Unix file systems and Linux environment setup, achieving a 30% increase in system reliability and reducing operational disruptions.

Pioneered automation continual process improvement in Linux-based processes and mount types, elevating operational efficiency by 35% and minimizing manual intervention. Cisco September 2022 – July 2023

Data Engineer

Engineered scalable ETL/ELT pipelines using AWS Glue, EMR (Spark), and Step Functions, enabling batch and near real-time data processing across multi-terabyte datasets.

Designed and maintained data lake and warehouse architectures on Amazon S3, Redshift, and Athena, leveraging partitioning, compression, and Spectrum to optimize query performance and reduce costs.

Built event-driven streaming pipelines with Kinesis Data Streams, Firehose, and Lambda, supporting real-time ingestion and analytics for high-volume transactional systems.

Automated infrastructure deployment with Terraform and CloudFormation, establishing reusable templates for serverless, containerized, and data processing workloads across AWS accounts.

Integrated machine learning workflows by enabling data pipelines that connected SageMaker, Redshift, and S3, streamlining model training, monitoring, and deployment at scale.

Implemented end-to-end security, monitoring, and governance frameworks using AWS IAM, CloudWatch, CloudTrail, and Lake Formation, ensuring compliance with enterprise and regulatory standards.

Created dynamic data visualizations and ETL patterns, facilitating real-time analytics and reducing data processing time by 30%.

Modernized permissions management and standard tools integration within Agile methodology, resulting in a 50% reduction in deployment errors and increased team productivity.

Architected complex data solutions utilizing Oracle Exadata and Perl, leading to a 25% improvement in query performance and enhanced data retrieval speed.

Kapital Information Technologies private limited Jun 2020 – August 2022 Data Engineer

Designed and orchestrated end-to-end ETL/ELT pipelines in Azure Data Factory (ADF), leveraging parameterization, dynamic pipelines, and metadata-driven frameworks to process multi-source structured and semi- structured datasets.

Built data lakehouse architectures using Azure Data Lake Storage Gen2 (ADLS) and Delta Lake, implementing bronze–silver–gold zones to support lineage, governance, and optimized query performance for analytical workloads.

Developed and optimized data models in Azure Synapse Analytics using partitioning, indexing, and materialized views, enabling faster query execution and reducing reporting time by 40%.

Designed real-time ingestion pipelines with Azure Event Hub and Stream Analytics, integrating with Databricks

(PySpark/Scala) for streaming transformations and pushing enriched data into Synapse and Power BI dashboards.

Conducted data profiling, cleansing, and enrichment using Databricks and SQL, applying advanced transformations

(windowing, ranking, incremental loads) to improve data quality and trustworthiness.

Implemented governance, monitoring, and security best practices across Azure using Azure Purview (Data Catalog), Role-Based Access Control (RBAC), and Key Vault, ensuring compliance with enterprise and regulatory standards.

Migrated existing Azure Data Factory and Synapse workloads into Microsoft Fabric, optimizing cost and simplifying governance under OneLake unified storage.

Revolutionized excellent written oral communication skills to drive cross-functional collaboration, improving project delivery timelines by 20% and aligning technical teams with business objectives.

Quantified system performance optimization through pipes and Perl scripting, achieving a 45% reduction in data processing latency and enhancing user experience significantly. EDUCATION

Master’s in information technology - University of Cincinnati

Bachelor’s in information technology - Shri Vishnu Engineering College for Women

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