Akhileswar Chinnaguravagari

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Data Engineer with around 4 years of experience in designing and optimizing scalable big data pipelines and cloud-based solutions using python, spark, SQL, AWS and AZURE technologies.
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Built and optimized end-to-end ETL pipelines using Azure Data Factory, Databricks (PySpark, SparkSQL, Scala), and Azure Data Lake, enabling scalable ingestion, transformation, and reporting across large and diverse datasets.
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In the Health Insurance project, worked on a SQL-centric architecture where all row-level business logic and transformation rules were stored and managed using SQL Server. Implemented complex transformations using SSIS packages, ensuring precise data processing and alignment with business rules.
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Migrated legacy SAS workflows to a modern cloud platform by rewriting business logic in Python, SparkSQL, and Scala, dramatically improving maintainability and performance.
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Leveraged SparkSQL extensively for both data transformation and reporting logic, supporting real-time and batch processing across high-volume datasets.
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Implemented parallel processing strategies in Databricks to accelerate pipeline execution, minimize latency, and optimize resource utilization for large-scale workloads.
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Designed metadata-driven pipelines and SQL-driven transformation logic to support modular and scalable ETL development, re- ducing manual overhead and enabling faster onboarding of new data sources.
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Processed and stored data in multiple formats including Parquet, JSON, XML, CSV, and Avro, with Parquet being the primary choice for optimized columnar storage and analytics performance.
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Developed full and incremental load pipelines for reliable and timely data refresh, enabling downstream analytics and self-service BI consumption.
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Applied rigorous data quality checks at multiple stages—validating schema, nulls, referential integrity, and transformation out- puts—ensuring trustworthiness of curated datasets.
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Secured ADF pipelines using Key Vault and parameterized configurations, ensuring scalable deployment across environments with minimal hardcoding.
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Migrated customer behavior and usage data into the cloud, enabling advanced analytics and segmentation strategies for marketing and operational reporting.
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Collaborated with BI developers and analysts to ensure seamless integration of transformed data into reporting platforms like Power BI and Tableau.
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Authored comprehensive technical documentation for SQL logic, Spark transformations, and ADF workflows to ensure clarity, main- tainability, and effective team collaboration.
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Used HiveQL in Hadoop-based environments for querying large datasets and validating transformation outputs during prototype and QA phases.
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Worked with both Azure and AWS platforms, leveraging components such as S3, Athena, and EMR during proof-of-concept and hybrid cloud initiatives.
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Delivered solutions following Agile methodology, participating in daily stand-ups, sprint planning, and backlog grooming to stay aligned with stakeholder priorities and deliverables.

Master’s in Management Information Systems (MIS)
Lamar University, Beaumont, Texas — Completed 2025

Focus: Data Engineering, Cloud (Azure/Databricks), ML/AI integration, MLOps

Highlights: Built metadata-driven pipelines, Spark/SQL workloads, and model deployment workflows; research assistant work on Amazon review analytics (AI- vs human-generated reviews, sentiment/rhythm analysis)

Relevant learning add-ons: Andrew Ng’s MLOps course (DeepLearning.AI), Mathematics for Machine Learning module