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  Over 3.2 years of combined experience in Azure and Aws Data Engineering.
  Expertise on Azure cloud platform tools like Azure Data Factory, Azure Databricks, ADLS Gen2, Azure Synapse, Azure SQL DB, Azure Key Vault, Logic Apps,Data Lake.
  Expertise on AWS cloud platform tools like AWS S3, AWS Data Lake, AWS RDS, AWS Glue, AWS EMR, AWS Athena, AWS Lambda, AWS Redshift, AWS Kinesis, AWS DynamoDB, AWS DMS, AWS Step Functions, AWS Batch, Databricks on AWS.
  Experiences on tools like Git, GitHub, Docker, PySpark, Azure CI/CD, Python, SQL, Power BI, Apache Spark.
  Utilizing AWS services for ETL pipelines, data warehousing, and data processing, leveraging Python and PySpark.
  Extensive experience in leveraging Azure Data Bricks for large-scale data processing, analytics.
  Demonstrated proficiency in orchestrating complex data workflows and ETL processes using Azure Data Factory pipelines.
  Proficient in building and tuning Apache Spark clusters within Azure Data Bricks for high-performance data processing and analysis.
  Expertise in integrating diverse data sources including on-premises and cloudbased systems for seamless data movement and transformation.
  Proficient in building and tuning Apache Spark clusters within Azure Data Bricks for high-performance data processing and analysis
  Developing serverless workflows and event-driven architectures using Azure Logic Apps and Azure Functions.
  Automated routine data processing tasks using SQL, R, and Python, reducing manual intervention and increasing operational efficiency.
  Expertise in architecting and implementing data lake solutions on Azure Data Lake Storage Gen2, enabling scalable storage and analytics for structured and
unstructured data.
  Implementing data governance and security protocols, ensuring compliance and safeguarding sensitive data within SQL databases.
  Strong understanding of security best practices and data protection mechanisms utilizing Azure Key Vault.
  Skilled in designing and implementing scalable and cost-effective storage solutions using Azure Blob Storage.
  Automating data processes and optimizing query performance, reducing processing times and operational costs through advanced technical strategies.
  Experience in utilizing Designed and implemented data ingestion pipelines from on-premise systems to AWS S3 using AWS Glue, Lambda, and Kinesis.
  Built PySpark ETL scripts on AWS EMR to transform and process large datasets for analytics teams.
  Currently utilizing SQL for data manipulation and querying within Aws environments, alongside working with databases like MySQL.
  Extensive proficiency in database management, encompassing SQL databases such as SQL Server, MySQL, and PostgreSQL, complemented by a deep understanding of NoSQL database like DynamoDB.
  Created Athena queries for ad-hoc analysis and reporting, improving business insights delivery by 40%.
  Developed and optimized Redshift data warehouse schemas, improving query performance by 50%.
  Integrated AWS CloudWatch alerts for data pipeline health monitoring and performance tuning.
  Experience with an in - depth level of understanding in the strategy and practical implementation of AWS Cloud-Specific technologies including EC2, EBS, S3, VPC, RDS, SES, ELB, EMR, ECS, Cloud Front, Cloud Formation, Elastic Cache, Cloud Watch, Red Shift, Lambda, SNS, Dynamo DB, Kinesis.
  Designed Glue Crawlers to automate schema discovery and catalog updates.
  Collaborated with analysts to build Power BI dashboards connected to data source of Azure and AWS.
  Detail-oriented Data Analyst with expertise in SQL and Python (NumPy, Pandas, Matplotlib, Seaborn), strong proficiency in Power BI, MySQL, and Advanced Excel Automated ETL workflows using Step Functions, reducing manual intervention by 80%.
  Hands-on experience using Jupiter Notebook for data analysis and visualization.
  ETL Developed KPI dashboards and performance metrics for senior management and stakeholders Integrated data from various sources including Teradata, SQL Server, Oracle, DB2, Netezza, and flat files for consolidated reporting.





Experienced Data professional with over 4+ years of expertise in data engineering and analysis. Skilled in developing architecture solutions, constructing ETL pipelines, and providing technical leadership. Proficient in data modeling and adept at leveraging various tools and technologies. Seeking roles as a Data Engineer to design robust data infrastructure or as a Data Analyst to analyze and derive insights from complex datasets.

Experienced Data Engineer with over 3 years of hands-on experience in designing, building, and optimizing cloud-based data solutions in fast-paced environments. Proven expertise in developing scalable ETL pipelines using Azure Data Factory, Databricks, and SQL, with a strong focus on data quality, governance, and automation. Known for proactively identifying and resolving data issues, improving pipeline reliability, and enabling real-time reporting through Power BI. Trusted collaborator across engineering and business teams with a flexible, solution-oriented mindset to deliver on project and team goals.


  Data Engineer with 7+ years building production-grade data platforms and Lakehouse architectures across banking, healthcare, and telecom, leading work end to end from requirements and design through release validation and production support.
  Implemented Azure and AWS Lakehouse platforms end to end using Azure Databricks, Delta Live Tables, Unity Catalog, Auto Loader, and Medallion Architecture, establishing reusable pipeline and data modeling standards teams could build on confidently.
  Built multi-cloud ETL and ELT pipelines across Azure (Data Factory, Databricks, ADLS Gen2, Synapse Analytics, Microsoft Fabric), AWS (Glue, EMR, Lambda, Step Functions, Redshift, Athena), and GCP (BigQuery), handling CDC, schema evolution, late-arriving data, and backfill strategies.
  Developed Python and Scala backend services with REST APIs, GraphQL, OAuth2/JWT authentication, PostgreSQL state management, and Redis caching, owning production observability through Datadog monitoring, incident response, and postmortems.
  Orchestrated complex workflows using Apache Airflow, Databricks Workflows, and AWS Step Functions, built governed ELT models using dbt and Snowflake, and partnered with analytics and business teams to define data contracts, data catalogs, and reduce recurring ad hoc requests.
  Established data quality and governance programs using Delta Live Tables Expectations, Great Expectations, and Soda across HIPAA, SOX, and GDPR-regulated pipelines for audit readiness, data lineage, and regulatory compliance.
  Translated ambiguous business requirements into well-scoped engineering deliverables, communicated progress clearly to finance, compliance, and clinical stakeholders, and managed competing priorities effectively across Agile sprint cycles.
  Set pipeline, data modeling, and coding standards in PySpark, Scala, Python, and SQL, mentored engineers at different levels, conducted code reviews, and drove CI/CD through GitHub Actions, Azure DevOps, and Terraform.


Results-driven dp-203 certified Azure Data Engineer with over 3 years of experience in designing and building scalable data pipelines and ETL solutions using Azure Data Factory, Databricks, and other cloud-based technologies. Proven expertise in handling large datasets, optimizing data workflows, and collaborating with cross-functional teams to deliver high-quality data solutions that drive business insights. Adept at implementing best practices in data integration, transformation, and automation.

Vimala Chinnasamy

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B.E - Computer Science and Engineering

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Data Engineer

Technology Analyst

B.E - Computer Science and Engineering

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