Akshay Kailasa

Data Engineer @ Dynamic Access Systems, Inc

About

I am a Senior Data Engineer with 5+ years of experience designing and optimizing cloud data solutions across Azure,databricks,AWS Glue. My expertise includes building transforming large datasets with PySpark and SQL, and enabling analytics through Power BI and Databricks. Spearheaded initiatives to enhance data architecture, significantly improving data accessibility, storage, and processing times by implementing robust data pipelines using SQL and Big data technologies.Key Skills:Programming Languages: Python, SQL, Scala, Java, PySpark.Database: Oracle DB,POSTGRESQL, Microsoft SQL Server, Snowflake, Hive, DynamoDB, MySQL,Mongo DBAnalytics: Tableau, Power BI, Advanced Excel, Google SheetsCloud Technologies: Azure (Azure Data Factory, Blob Storage, ADLS Gen2, Cloud functions, IAM),AWS (EC2, S3, Redshift, Step function, AWS Athena, EMR, Lambda function, RDS, SQS, ECS, Glue, DynamoDB, AWS API, Airflow,Event Bridge,I AM), DBT(Models,Snapshots,Macros,Analyses,Sources,Documentation,Seeds,Singular and Custom test), DatabricksBig Data Technologies: Apache Spark, Hadoop,Hive,MapReduce,pyspark, Apache Flink.Project Management: Agile, DevOps, Jira, ConfluencePortfolio: https://akshay033333.github.io/ GitHub: https://github.com/akshay033333

Country

City

United States

Industry

Information Technology & Services

Skill

Azure Databricks, Oracle Database, Change Data Capture, Dataproc, Google Cloud Dataflow, Google spanner, Informatica, Apache Beam, Data Loading, SQL Server Management Studio, Identity and Access Management (IAM), Data Build Tool (DBT), SQL , dbt core, Databricks, Gitlab, AWS Glue, AWS Lambda, Amazon Relational Database Service (RDS), Azure Data Factory

Experience

Data Engineer

Dynamic Access Systems, Inc

2026-4 - Present · 6 mos

Tampa, FL

Big Data Engineer

Raven Software Solutions Inc.

2023-5 - Present · 3 yrs 5 mos

United States

Design and implement a unified Lakehouse architecture using Azure Databricks and Delta Lake to process Healthcare claims, provider, and member data. Data is ingested from on-prem SQL Server and APIs into Azure Data Lake Storage Gen2 via ADF, transformed in Databricks with Pyspark, and exposed for analytics through Databricks SQL warehouse to Power BI. Developed ETL/ELT pipelines using SparkSQL and PySpark, incorporating SCD2 and Databricks, resulting in a 33% increase in data retrieval speed and a 25% reduction in load times. - Enhanced SQL query performance through advanced techniques (joins, indexes, window functions), achieving a 20% reduction in query time. - Automated data ingestion and quality checks with PyTest, reducing bug rates by 15% and improving deployment reliability. - Leveraged Azure Cloud Services (Databricks, DevOps, Key Vault) and Apache Spark for data transformation, leading to a 10% improvement in processing efficiency. - Managed and integrated various data formats (ADLS, Delta Tables, JSON, CSV, Parquet), ensuring 100% data accuracy and consistency.

Cloud Data Engineer

NStarX

2018-11 - 2021-12 · 3 yrs 2 mos

India

Built custom web crawlers to automate license verification data collection across multiple sources, cutting manual verification effort by 40% and saving 400+ hours/year that the team previously spent on manual lookups. Saved analysts ~30 hours/month by re-architecting ETL pipelines with AWS Glue for automated data cataloging and Python-driven downstream loading — reducing report retrieval time by 20%. Eliminated billing calculation errors across 100+ providers by developing PL/SQL stored procedures, functions, and views in SQL Server for complex healthcare calculations including total billing per provider, per location, and per payer.Cut ETL execution time by 20% for 100+ providers by tuning SSIS workflows — optimizing CASE statements, UNION operations, complex joins, CTEs, and Derived Columns to improve data retrieval throughput. Reduced feed processing time by 30% by migrating legacy feed processes to AWS cloud using Python, Glue ETL jobs, Lambda, Step Functions, DMS, and S3 — fully eliminating the on-prem dependency. Enabled seamless data flow across the organization by integrating disparate data sources into a unified pipeline architecture, reducing data silos and supporting faster decision-making for enrollment workflows.