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Data Engineer with 10+ years of experience in designing and delivering large-scale data applications leveraging Big Data technologies and Cloud based Data engineering platforms. Hands on experience on Unified Data Engineering with Azure Data Factory, AWS, GCP, Snowflake, Databricks, Designed and developed Databricks Notebooks leveraging Python, Apache Spark, and SQL to build scalable data pipelines. Designed and implemented end-to-end data processing pipelines using Azure Data Factory, Databricks, Azure Functions, Triggers and Azure Key Vault to address complex business use cases and enable real-time analytics and monitoring. Good understanding of Spark Architecture with Databricks, Structured Streaming. Setting Up Integration Azure Data Factory, AWS and GCP with Databricks Workspace for Business Analytics, Manage Clusters in Databricks and managing the Machine Learning Lifecycle. Implemented Data Encryption and Access Control Measures using Azure Key Vault, Azure AD, and Microsoft Defender for Cloud, securing sensitive data and credentials. Built a robust and scalable data pipeline using AWS services including S3, Glue, Redshift, Lambda, CloudWatch, Kinesis, Step Functions, and Secrets Manager to orchestrate ETL workflows, manage secure data access, and support business intelligence reporting. Designed and delivered fact and dimension tables to support BI reporting and executive-level visualization in Power BI and Tableau, enabling data-driven decision-making for key management. Skilled in query optimization, data workflows using Alteryx and data visualization presentations using Power BI, and Quick Sight. Built pipelines in ADF using Datasets/Linked Services/Pipeline to extract, load and transform data from various sources such as Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool, and backwards. Developed scalable ETL pipelines on GCP using Dataflow, Big Query, and Cloud Storage to ingest, process, and transform data from sources like Cloud SQL, on-premises databases, Pub/Sub, and SaaS platforms automated workflows with Cloud Functions and Cloud Composer. Built robust data pipelines in Snowflake using Streams, Tasks, and Snowpipe to extract, load, and transform data from diverse sources such as AWS S3, Azure Blob Storage, on-prem SQL systems, and third-party APIs; enabled near real-time ingestion and transformation for analytics and reporting. End-to-end data pipelines using AWS Glue, Lambda, and Step Functions to extract, transform, and load data from Amazon S3, RDS, Redshift, and external APIs, ensured data quality and orchestration for both batch and near real-time processing scenarios. Migrated on-premises data from SQL and Oracle to Azure Data Lake Storage (ADLS Gen2) using Azure Data Factory ensuring scalable and efficient cloud data integration. Good knowledge of batch and historical data processing using Hive, Pig, and Databricks, enabling retrospective trend analysis, advanced feature engineering, and generation of ML-ready datasets for predictive modeling. Proficient in implementing CI/CD frameworks for data pipelines using tools like Jenkins, ensuring efficient automation and deployment. Configured data quality checks using Great Expectations, providing real-time monitoring and automatic alerting when validation rules were violated. Proficient in cloud migration strategies with a focus on Azure, AWS, Snowflake and GCP, including Azure Migrate, Lift and Shift methodologies. Managed end-to-end deployment workflows for data processing jobs, ensuring smooth transitions from development to production environments using Bamboo. With experience in delivering business intelligence solutions, developing executive dashboards and self-service BI using Tableau, Power BI, Looker, and Looker Studio to visualize KPIs, operational metrics, and compliance outcomes. Proficient in sorting, analyzing, and integrating data using GCP (Big Query, Cloud Dataflow), AWS (Glue, Redshift), and Snowflake (SQL, Streams & Tasks, Snow pipe). Proficient in configuring Azure DevOps pipelines and GitHub for continuous integration, automated testing, and streamlined code delivery. Experienced in dimensional modeling, data migration, cleansing, and ETL processes for data warehousing. Skilled in statistical modeling, machine learning, and decision trees, with a strong foundation in ETL processes and data transformation in AWS, GCP, Snowflake and Azure. Experienced in developing, maintaining, and supporting data pipelines using Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), Azure Synapse Analytics, IBM DataStage jobs, custom frameworks, and related technologies. Worked in the areas of analysis, design, development, production support and implementation phases of Datawarehouse and BI application. Extensively worked on Datawarehouse concepts like Data Modeling, ETL Jobs, Data Marts and ETL Framework. Have extensive experience with Power BI, encompassing data visualization, report creation, and dashboard development. Hands-on experience in designing and implementing ETL/ELT pipelines to ingest semi-structured and nested JSON data from MongoDB and MongoDB Atlas into cloud data warehouses like Snowflake. Proficient in utilizing Spark Core and Spark SQL scripts using Scala to accelerate data processing capabilities. Good Experience in SQL (SSIS, SSAS, SSRS), T-SQL, PLSQL, Unix, Microsoft Power BI, Office 365.

Around 9+ years of experience in Data Engineering, working on building end-to-end data solutions that involve ingestion, transformation, modeling, and reporting for large-scale systems across healthcare, retail, and finance domains.
Hands-on experience with big data technologies like Apache Spark, Hadoop, and Kafka, working on both batch and real-time processing workflows for operational and analytical use cases.
Skilled in cloud environments including AWS, Azure and GCP with solid experience working on services like S3, Lambda, Glue, Redshift, EC2, Athena, Azure Data Lake, Bigquery and Data Factory.
Developed robust and reusable ETL pipelines using Python, PySpark, and SQL to transform raw data into structured formats and deliver it to data lakes and data warehouses.
Proficient in building data models and architecting database solutions with an understanding of normalization, denormalization, schema design, and performance optimization.
Built and maintained data workflows that process high-volume transactional data using tools like Spark and Glue, with strong focus on reliability, performance, and maintainability.
Integrated Kafka for real-time streaming use cases, setting up topics, producers, and consumers for event-driven architectures and near real-time alerting and analytics.
Worked on automating data pipeline deployments and infrastructure changes using Terraform, Jenkins, and GitHub in CI/CD workflows to ensure faster, repeatable deployments.
Applied machine learning models within data workflows using AWS SageMaker and Azure ML Studio, helping automate predictions for business operations and reporting.
Designed serverless pipelines using AWS Lambda and event-based triggers, building lightweight and scalable solutions for data ingestion and processing.
Experience working with data stored in S3, Azure Blob, and ADLS Gen2 with a focus on organizing storage, access control, and encryption practices for compliance and security.
Implemented and managed data warehouse solutions in Snowflake and Redshift, using best practices around partitioning, clustering, and query optimization for better performance.
Used DBT for data modeling and building semantic layers, and Airflow to orchestrate ETL and ELT pipelines with proper scheduling, monitoring, and error handling.
Built dashboards using Tableau and Power BI, integrating them with cloud databases and providing business users with live insights and drill-down capabilities.
Extracted and processed data from APIs and various RDBMS sources including PostgreSQL, MySQL, and SQL Server for downstream reporting and machine learning tasks.
Managed version control using Git, participated in code reviews, and worked in Agile teams with tools like Jira to deliver sprint-based work and collaborate across teams.
Applied Spark performance tuning techniques, such as caching, partition control, and memory tuning to improve data processing speed and cluster efficiency.
Proficient in PostgreSQL and other RDBMS for schema design, query optimization, and performance tuning.
Skilled in Docker and Kubernetes for containerized deployments and microservice orchestration.
Extensive experience with Apache Kafka for real-time streaming, event-driven data ingestion, and alerting use cases.
Supported DevOps and monitoring efforts using CloudWatch, ELK, and Prometheus to ensure pipeline visibility, logging, and real-time alerts for failures or delays.
Designed microservice-based data applications and deployed them in Kubernetes environments using Docker and Helm for container orchestration.
Supported data security, compliance, and access control using IAM roles, KMS, and encryption policies across cloud platforms.
Maintained documentation in Confluence for pipeline logic, schedule, ownership, and known edge cases, making transitions and onboarding easier.
Regularly collaborated with business stakeholders, data scientists, and engineering teams to translate requirements into scalable data solutions and ensure alignment between platform design and business goals.

Senior Data Engineer with 11+ years of experience designing and delivering scalable ETL pipelines and cloud-based data platforms. Strong expertise in Python, SQL, PySpark, Apache Airflow, AWS, and Snowflake, with a proven ability to transform complex data into reliable, analytics-ready datasets. Collaborative problem-solver with deep experience supporting BI, and data-driven decision-making across enterprise teams.

Data Engineer with over 10 years of experience in designing, implementing, and optimizing data solutions within Retail, Telecom, Banking and Healthcare domains. Expertise includes Snowflake, ETL development using Talend and Informatica, and Enterprise Data Warehousing. Proven track record of delivering scalable and efficient data architectures aligned with business needs.

Seasoned Senior Data Engineer with over 9.5+ years of experience serving clients in the USA and Canada, specializing in the insurance, banking, and finance domains. Expertise in ETL tools such as DBT, Snowflake ADF, and Informatica, alongside proficiency in Oracle 12G and UNIX environments. Skilled in analyzing business requirements and implementing multi-dimensional data projects, while maintaining high standards of quality and speed. Excellent interpersonal skills and ability to manage critical situations independently, ensuring effective collaboration with team members and stakeholders.

Vinayak Sutar

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Manager - Senior Data Engineer

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Master of Computer Applications - Computer Applications

Bachelor of Science - Chemistry

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Manager - Senior Data Engineer

Project Lead

Master of Computer Applications - Computer Applications

Bachelor of Science - Chemistry

HSC -

SSC -

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