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Sai Verry Keerthan - Data Engineer

[email protected]

Location: Dallas, Texas, USA

Relocation: Any Location

Visa: OPT

Resume file: SaiVarre_Data_Engineer_Resume_1772725626203.pdf Please check the file(s) for viruses. Files are checked manually and then made available for download.

Sai Varre Richmond, VA | [email protected] | +1(346)-285-2722 |LinkedIn |Trailhead Summary Data Engineer with 4+ years of experience designing, building, and optimizing batch and streaming data pipelines across Azure, AWS, and GCP environments. Hands-on with Python, SQL, Spark, Kafka, Airflow, dbt, and Snowflake, delivering scalable ETL/ELT workflows and high-quality analytical data models supporting enterprise reporting. Skilled in implementing data quality validation, schema enforcement, and orchestration reliability using Great Expectations, Kafka Schema Registry, and parameterized Airflow pipelines. Experienced partnering with analytics and ML teams to operationalize production-ready dataflows, improve reporting accuracy, and support predictive and anomaly detection use cases through modern DataOps practices and governed data infrastructure. Work Experience AT&T Data Engineer Cloud Data Platform | ETL Orchestration | DataOps & Observability | Telecom Analytics May 2023 - Present Engineered and maintained scalable real-time ETL pipelines using Azure Data Factory, AWS Glue, and Kafka, processing 10+ TB/month of telecom data (Parquet/Delta) for analytics dashboards. Implemented Spark Streaming and Snowflake Streams frameworks to achieve sub-minute latency for network performance analytics and monitoring. Contributed to standardized ingestion + transformation patterns across Azure, GCP, and Snowflake environments, improving code reusability and reducing onboarding time for new pipelines. Automated data validation and quality checks using Great Expectations, detecting schema drift and null anomalies, and reducing data quality incidents by 35%. Introduced producer consumer data contracts using Kafka Schema Registry and CI checks (backward-compatibility, required fields), preventing breaking changes before deploys. Developed and automated modular dbt transformations for 200+ business tables (with CI/CD), supporting customer behavior, fraud detection, and predictive maintenance analytics. Deployed cross-cloud pipelines using GCP Dataflow, Cloud Storage, BigQuery and Cloud Composer, automating ingestion from Azure Data Lake and enabling unified, monitored analytics across environments. Configured Pub/Sub connectors for near real-time synchronization between Kafka and BigQuery, improving multi-region data accessibility and query performance by 20%. Added table-level lineage and run-history metadata logging for batch and streaming pipelines, improving traceability and speeding up root-cause analysis during data incidents. Improved Snowflake performance and reduced cloud costs by ~20% through warehouse tuning and optimized query caching. Enhanced data observability with Grafana, Azure Monitor and Power BI dashboards to monitor pipeline throughput, SLA breaches, and error trends across distributed workloads. Collaborated with Data Scientists to operationalize ML pipelines within Databricks, integrating predictive churn and anomaly detection models into production dataflows. Created parameterized Airflow DAGs to manage cross-cloud dependencies among Snowflake, Redshift, and PostgreSQL workloads, improving reliability and automation. Enhanced pipeline reliability by introducing fault-tolerant execution patterns, including retry handling and targeted backfills, which reduced on-call intervention during pipeline failures. Partnered with product teams to define telecom data governance standards cataloging, lineage tracking, and access controls using Azure Purview and AWS Glue Data Catalog. Authored runbooks, support playbooks, and post-incident documentation, and coordinated RCA review sessions, improving onboarding, knowledge transfer, and support handoffs across data teams. BluePal Solutions Pvt Ltd, India Data Engineer Nov 2020 - Aug 2022 Built Python ETL pipelines to process academic and financial data into PostgreSQL, reducing manual reporting work by 40%. Designed PostgreSQL schemas with indexes and partitions, cutting query latency from 5s+ to under 2s for high-volume analytics queries. Integrated 10+ university systems with secure REST APIs, enabling consistent data exchange for enrollment, finance, and student management. Automated ETL testing and validation with Python, SQL, and JUnit, improving trust in nightly pipelines and reducing defects by 25%. Developed Kafka producers and consumers to stream enrollment and attendance events in real time, providing administrators timely operational insights. Containerized ETL workloads with Docker and Jenkins CI/CD, improving consistency across environments and cutting deployment effort by half. Implemented Python + Great Expectations validation suites for nightly ETL jobs, reducing reporting errors by 25% and improving PostgreSQL data reliability. Migrated legacy academic data pipelines to GCP BigQuery (sandbox) and Cloud Storage for benchmarking, introducing cost-efficient analytics at scale. Collaborated with BI teams and delivered 5+ forecasting dashboards for enrollment planning (used by 200+ administrators) to improve resource allocation. Supported deployment of containerized ETL services to Azure App Service using Azure DevOps pipelines, contributing to the team s early cloud migration efforts. Designed role-based access and basic data-anonymization scripts in Python/SQL to secure sensitive student records. Wrote technical documentation and onboarding guides, reducing new engineer ramp-up time by 30% and promoting best practices. Monitored production by analyzing PostgreSQL logs and Kafka streams, diagnosing bottlenecks and sustaining 99.9% pipeline uptime. Technical Skills Programming Languages: Java, Python, Shell, C, C++, Bash, Scala, SQL, YAML Data Engineering & Big Data: ETL/ELT, ETL Testing, Apache Kafka, Apache Spark, Hadoop, Airflow, dbt, Delta Lake, Databricks, Great Expectations, DataOps, Data Modeling Frameworks / Libraries: Flask, TensorFlow, PyTorch, Scikit-learn, spaCy, NLTK, BERT, PySpark Cloud & DevOps: AWS (S3, Glue, Kinesis, Lambda, CDK, CloudWatch, CodePipeline, CodeBuild), Azure (ADF, Synapse, Azure DevOps, Purview, Monitor), GCP (BigQuery, Dataflow, Pub/Sub, Composer), Docker, Jenkins, Terraform Databases & Warehousing: PostgreSQL, MySQL, Oracle, MongoDB, Snowflake, Redshift, BigQuery, NoSQL Testing & Monitoring & Logging: ELK Stack (Elasticsearch, Logstash, Kibana), Datadog, CloudWatch (logs, metrics, alarms) Data Analysis & Visualization: Tableau, Power BI, Excel (Advanced), Matplotlib, Seaborn Testing Tools: JUnit, PyTest, JMeter, REST Assured (for pipeline/API testing) Development & Collaboration Tools: Git, GitHub, Bitbucket, Jira, Confluence, Jupyter, Pandas, NumPy, TKinter Education University of Wisconsin-Milwaukee Master s in Computer Science May 2024 Related Coursework: Natural Language Processing, Robot Motion Planning, Machine Learning, Computational Intelligence, Computer Networks, Computational Models Decision Making, Operating Systems, Data Analytics Jawaharlal Nehru Technological University, Hyderabad B.Tech in Computer Science Engineering Nov 2020 Keywords: cprogramm cplusplus continuous integration continuous deployment machine learning business intelligence sthree Virginia