Snowflake Data Engineer - ETL/ELT & Snowpark

Zalakben Bhadani GC Holder Los Angeles, CA

347-***-**** ************@*****.*** https://www.linkedin.com/in/zalakbhadani/

PROFESSIONAL SUMMARY:

Experienced Snowflake Data Engineer with 7+ years of expertise in designing, developing, and optimizing scalable cloud-based data platforms, ETL/ELT pipelines, and enterprise data warehouses across AWS and Azure environments. Proven experience in Snowflake, Snowpark, Azure Databricks, Azure Data Factory (ADF), dbt, PySpark, Python, Kafka, and Hadoop ecosystems for building high-performance data solutions. Skilled in data migration, real-time and batch data processing, dimensional modeling, data lake architectures, and handling structured, semi-structured, and unstructured data at scale.

Strong expertise in implementing Snowflake features including Snowpipe Streaming, Streams & Tasks, Dynamic Tables, RBAC, Time Travel, Zero Copy Cloning, Snowpark for Python, and performance optimization. Hands-on experience integrating Snowflake with enterprise applications such as Salesforce, HubSpot, and other SaaS platforms using APIs, Fivetran, Matillion, Informatica, and Talend. Proficient in developing modern ELT frameworks using dbt and implementing CI/CD automation through GitLab and Infrastructure as Code (IaC).

Experienced in building AI/ML-ready data pipelines leveraging Snowpark, Databricks, SageMaker, and Vertex AI to support advanced analytics, predictive modeling, and Generative AI initiatives. Adept at implementing data governance, security, compliance, and monitoring solutions using Azure Key Vault, CloudWatch, and enterprise security best practices. Strong background in Agile environments with a proven ability to deliver scalable, reliable, and business-driven data engineering solutions.

TECHNICAL SKILLS:

Cloud Platforms & Data Warehousing: Snowflake, AWS, Microsoft Azure, GCP (BigQuery, Vertex AI).

Data Engineering & ETL/ELT: dbt, Matillion, Fivetran, Informatica PowerCenter, Talend, Azure Data Factory (ADF), AWS Glue, Apache Airflow, CI/CD Pipelines, Data Migration & Modernization.

Big Data & Streaming Technologies: Apache Spark, PySpark, Apache Kafka, Apache Flink, Hadoop Ecosystem, Delta Lake, Delta Live Tables (DLT).

Programming & Scripting: Python, SQL, Java, Unix Shell Scripting (Bash).

Databases & Data Storage: Snowflake, PostgreSQL, MySQL, DynamoDB, Amazon S3, Azure Data Lake Storage Gen2, Amazon Redshift.

Data Modeling & Architecture: Dimensional Modeling (Star & Snowflake Schema), Data Lakehouse Architecture, Data Warehousing, Data Integration, Data Governance, Metadata Management.

AI/ML & Advanced Analytics: Snowpark for Python, SageMaker, Vertex AI, Databricks ML, AI/ML Data Pipelines, Generative AI Data Engineering, Feature Engineering.

DevOps & Infrastructure as Code: Git, GitLab, Jenkins, Terraform, Docker, Kubernetes.

Visualization & Reporting: Tableau, Power BI, Looker.

Monitoring, Security & Governance: CloudWatch, Data Quality Validation, Azure Key Vault, IAM, RBAC, HIPAA Compliance, Data Governance & Security.

PROFESSIONAL EXPERIENCE:

Data Engineer (Snowflake & Azure) Ensign Services, CA, Remote Mar 2026 – Till Date

Designed and developed scalable ETL/ELT pipelines using Azure Data Factory (ADF) and Snowflake to ingest, transform, and load data from multiple enterprise source systems.

Implemented Snowflake-native data ingestion frameworks using Snowpipe Streaming, Streams, and Tasks to support near real-time and batch data processing.

Developed and optimized Snowflake databases, schemas, tables, views, stored procedures, and complex SQL transformations to support enterprise analytics and reporting.

Built scalable data lake and lakehouse solutions using Azure Data Lake Storage Gen2 (ADLS Gen2), enabling efficient processing of structured, semi-structured, and unstructured data.

Designed and maintained enterprise data pipelines using Python, SQL, Azure Data Factory, and Snowflake to ingest and process patient, clinical, claims, EMR/EHR, and operational healthcare data from multiple internal and third-party systems.

Built scalable ELT frameworks to cleanse, transform, aggregate, and validate structured and semi-structured healthcare datasets supporting enterprise analytics and regulatory reporting.

Developed optimized Snowflake databases, views, stored procedures, and SQL transformations supporting population health, revenue cycle, provider performance, and operational reporting.

Created Tableau dashboards for patient outcomes, claims processing, hospital operations, quality metrics, and executive KPI reporting.

Integrated healthcare applications using REST APIs, HL7/FHIR interfaces, JSON, and cloud storage services to deliver unified analytical datasets

Collaborated with Product Managers, Business Analysts, Clinical Informatics teams, and Data Architects to define enterprise data management strategies and reporting requirements

Developed reusable Python libraries for automated data validation, duplicate detection, anomaly identification, and data quality monitoring.

Built reusable REST APIs and data services enabling secure access to healthcare analytics datasets consumed by enterprise reporting applications.

Identified and resolved ETL pipeline performance bottlenecks by optimizing SQL transformations, Snowflake warehouse utilization, and data loading strategies.

Automated enterprise reporting workflows supporting operational metrics, provider performance, patient outcomes, and executive dashboards.

Optimized Snowflake query performance through clustering, warehouse tuning, materialized views, and workload management, improving dashboard response times by over 35%.

Built CI/CD pipelines using Azure DevOps, Git, and Jenkins to automate deployment, testing, and migration of Snowflake database objects.

Created technical documentation including source-to-target mappings, API documentation, architecture diagrams, operational runbooks, and best practices.

Supported enterprise reporting initiatives by delivering analytics-ready datasets consumed by Tableau and Power BI.

Leveraged Snowpark for Python and PySpark to perform advanced data transformations, cleansing, enrichment, and feature engineering for downstream analytics and AI/ML initiatives.

Developed reusable dbt models, incremental load strategies, and automated data quality validation frameworks to improve pipeline reliability and maintainability.

Optimized Snowflake performance through warehouse sizing, clustering strategies, query tuning, partition pruning, and workload management techniques.

Integrated data from cloud and SaaS applications using REST APIs, JSON, and automated ingestion pipelines to create unified enterprise datasets.

Designed dimensional and star-schema data models to support business intelligence, self-service analytics, and operational reporting requirements.

Collaborated with business stakeholders, data architects, and analytics teams to gather requirements and deliver scalable cloud-based data solutions.

Implemented Snowflake security best practices including Role-Based Access Control (RBAC), secure data sharing, data masking, and governance policies.

Supported enterprise reporting and dashboard solutions by integrating Snowflake datasets with Power BI and other visualization platforms.

Developed CI/CD deployment pipelines using Azure DevOps, Git, and Infrastructure-as-Code (IaC) practices to automate migration and release processes.

Participated in cloud data migration initiatives, modernizing legacy data warehouse workloads and on-premises applications onto Snowflake and Azure platforms.

Monitored data pipelines, resolved production incidents, performed root cause analysis, and ensured high availability and reliability of data platforms.

Created technical documentation, data lineage artifacts, source-to-target mappings, and operational runbooks to support ongoing maintenance and governance.

Contributed to Agile/Scrum ceremonies including sprint planning, backlog grooming, code reviews, and release management activities.

Environment: Snowflake, Snowpark, Snowpipe Streaming, Streams & Tasks, Azure Data Factory, Azure Databricks, Azure Data Lake Storage Gen2, dbt, PySpark, Python, SQL, Microsoft Fabric, Power BI, Azure DevOps, Git, REST APIs, JSON, CI/CD, Agile/Scrum.

Data Engineer/Snowflake Data Engineer Hertz, Remote Sep 2024 – Feb 2026

Collaborated with Business Analysts, Data Architects, and Product Owners to gather requirements and design scalable cloud-based data solutions supporting enterprise analytics and operational reporting.

Designed and developed end-to-end ETL/ELT pipelines using Azure Data Factory (ADF), Azure Databricks, PySpark, and Snowflake to process large-scale structured and semi-structured datasets.

Built scalable data ingestion frameworks using Snowpipe, Snowflake Streams & Tasks, and cloud storage integrations to support batch and near real-time data processing.

Developed and optimized PySpark applications within Azure Databricks to perform complex data transformations, cleansing, enrichment, and aggregation.

Engineered real-time data pipelines using Apache Kafka, PySpark, and Snowflake, enabling low-latency data processing for business-critical applications.

Integrated Snowflake with Azure OpenAI and Databricks ML environments to support AI-driven analytics, automated insights generation, and predictive modeling use cases.

Designed scalable cloud-native data pipelines using Python, SQL, Snowflake, Azure Databricks, and Azure Data Factory to process reservation, booking, fleet, customer loyalty, and rental transaction data.

Built enterprise ELT pipelines integrating reservation systems, CRM platforms, payment gateways, and third-party travel APIs into Snowflake for enterprise analytics.

Built enterprise data pipelines that aggregated reservation, fleet, CRM, and customer loyalty data from multiple internal and third-party systems.

Created Tableau dashboards supporting executive reporting, operational metrics, fleet utilization, booking trends, and customer engagement analysis.

Developed comprehensive technical documentation including API specifications, data lineage, architecture diagrams, and operational runbooks.

Developed Python-based transformation frameworks to clean, aggregate, enrich, and standardize travel and customer behavioral datasets.

Created interactive Tableau dashboards for fleet utilization, reservation trends, customer loyalty, revenue analytics, booking conversions, and operational KPIs

Integrated REST APIs, JSON services, Salesforce, and external travel partner systems to consolidate enterprise reporting data.

Designed reusable Snowflake data models supporting business intelligence, customer analytics, and executive reporting.

Optimized SQL queries, Snowflake virtual warehouses, clustering keys, and materialized views, significantly improving reporting performance

Collaborated with Product Managers, Marketing teams, Operations, and Business Analysts to define enterprise-wide reporting and data management strategies.

Developed CI/CD pipelines using GitHub Actions, GitLab, and Jenkins for automated testing, deployment, and database promotion across environments.

Implemented Apache Airflow workflows for orchestrating end-to-end data ingestion, transformation, and reporting pipelines.

Developed and maintained dbt models using layered architecture, implementing incremental loading, dimensional modeling, and data quality validations.

Created reusable and parameterized data transformation frameworks to improve pipeline maintainability, scalability, and performance.

Designed and implemented Airflow DAGs to orchestrate complex workflows across Snowflake, Databricks, SaaS applications, APIs, and cloud storage platforms.

Built and scheduled ELT workflows using Matillion and Snowflake, leveraging push-down processing capabilities to optimize transformation performance.

Automated data ingestion and workflow execution using AWS Lambda, reducing manual operational effort and improving reliability.

Migrated legacy data warehouse workloads from Oracle, Redshift, and on-premises platforms to Snowflake with minimal downtime and business disruption.

Implemented Change Data Capture (CDC) and incremental data loading strategies to improve data freshness and reduce processing overhead.

Designed dimensional and star-schema data models to support business intelligence, self-service analytics, and enterprise reporting initiatives.

Developed REST API integrations and data services to facilitate seamless data exchange between enterprise applications and analytics platforms.

Integrated data from multiple enterprise systems, databases, cloud platforms, and third-party applications to create unified analytical datasets.

Optimized Snowflake performance through warehouse tuning, clustering strategies, query optimization, and workload management techniques.

Implemented data governance, RBAC security controls, IAM policies, and secure external stages to ensure regulatory compliance and data protection.

Built CI/CD pipelines using GitLab, GitHub, Azure DevOps, and Jenkins to automate deployment, testing, and release management processes.

Utilized Terraform, Docker, and Kubernetes to automate infrastructure provisioning, application deployment, and environment management.

Developed monitoring and alerting solutions using Azure Monitor, Log Analytics, KQL, and Snowflake monitoring tools to proactively identify and resolve data pipeline issues.

Created interactive dashboards and reports using Power BI, Tableau, and Looker by integrating directly with Snowflake datasets.

Performed data quality validation, reconciliation, root cause analysis, and troubleshooting to ensure data accuracy, consistency, and reliability.

Environment: Snowflake, Snowpark, Snowpipe, Streams & Tasks, dbt, Azure Data Factory (ADF), Azure Databricks, PySpark, Apache Kafka, Apache Airflow, Matillion, Python, SQL, Azure Data Lake Storage Gen2 (ADLS Gen2), AWS S3, AWS Lambda, Terraform, Docker, Kubernetes, GitLab CI/CD, Azure DevOps, Power BI, Tableau, Looker, REST APIs, KQL, Agile/Scrum.

Snowflake Data Engineer Australia Post, Melbourne, Australia Sep 2023 – Sep 2024

Designed and optimized Snowflake ELT pipelines using Snowpipe, Streams, and Tasks, enabling automated and near real-time ingestion from AWS S3.

Accelerated data platform deployment speed by 40% by implementing Infrastructure as Code (Terraform + CloudFormation) and automated CI/CD pipelines for Snowflake and AWS services.

Migrated and integrated data from Amazon Redshift to Snowflake, improving query performance and scalability while reducing maintenance overhead.

Orchestrated BigQuery data pipelines using Airflow, enabling multi-cloud support.

Connected Snowflake datasets to Power BI via Microsoft Fabric for real-time reporting.

Integrated Apache Kafka with Snowflake using Kafka Connect and Snowpipe Streaming API for near real-time data ingestion.

Managed end-to-end ELT pipelines integrating multiple sources (APIs, flat files, RDBMS, Kafka) into Snowflake for unified data access.

Developed Tableau dashboards and analytical reports for shipment tracking, operational KPIs, logistics performance, and customer delivery metrics.

Designed REST API integrations to exchange shipment and operational data between enterprise systems and Snowflake.

Built dbt-based transformation frameworks on Snowflake, implementing modular models, testing, and documentation to improve data quality and maintainability.

Developed Snowpark (Python) data processing jobs to execute complex transformations directly within Snowflake, minimizing data movement.

Migrated batch processing workflows from Hadoop ecosystem to Snowflake using Python and SQL.

Implemented CI/CD pipelines for Snowflake deployments using GitLab/GitHub Actions, automating schema changes, role management, and code promotion across environments.

Designed event-driven data pipelines using Kafka and Kinesis, feeding Snowflake for low-latency analytics use cases.

Integrated Snowflake with downstream analytics and BI platforms, enabling self-service analytics and faster business insights.

Monitored and optimized data pipelines using CloudWatch, Grafana, and Snowflake query profiling, proactively identifying bottlenecks.

Configured AWS Lambda functions and Cloud Watch Events to schedule dbt runs, trigger Glue jobs, and automate failure notifications via SNS for operational monitoring.

Used Snowflake Streams and Tasks for near real-time data refreshes; applied clustering and partitioning for high-performing analytical queries across large datasets.

Used Python with Pandas, SQLAlchemy, and PySpark, Snowpark to model, cleanse, and normalize raw data for scalable data pipelines.

Leveraged Hadoop ecosystem components (HDFS, YARN, Hive, HBase, Oozie, Sqoop, Pig) to deliver end-to-end Big Data solutions.

Automated infrastructure provisioning for data platforms using Terraform and CloudFormation, reducing manual errors and accelerating environment setup.

Collaborated with analytics, ML, and business teams to deliver analytics-ready and AI/ML-consumable datasets.

Supported high-volume, large-scale datasets with billions of records, ensuring reliability, fault tolerance, and performance at scale.

Applied strong problem-solving skills to troubleshoot complex data pipeline failures and query performance issues across Snowflake and AWS ecosystems.

Data Engineer AnTim Technologies LLP, Surat, India Jan 2019 – June 2023

Partnered with product managers and marketing analysts to understand customer journey and conversion funnel metrics and translated them into modular dbt models.

Ingested clickstream, CRM, and transaction data into AWS S3 using PySpark-based AWS Glue jobs, improving data reliability and accuracy.

Automated data cleansing, deduplication, and enrichment pipelines using Python and Spark, improving data quality by 25%.

Developed GraphQL APIs for high-performance data access in Microservices, reducing API response times across analytics tools.

Migrated PySpark workflows to AWS Glue, optimizing runtime and scalability for production workloads.

Integrated Apache Kafka with Snowflake using Kafka Connect and Snowpipe Streaming API for near real-time data ingestion.

Managed end-to-end ELT pipelines integrating multiple sources (APIs, flat files, RDBMS, Kafka) into Snowflake for unified data access.

Integrated data pipelines with GCS, BigQuery, and Cloud Functions, supporting cross-cloud analytics capabilities.

Managed source code versioning with GitHub, created pull requests for peer reviews, and integrated CI/CD pipelines for automated dbt model testing and deployment.

Designed encryption and masking routines for PII/PCI data to meet GDPR and CCPA compliance within Snowflake.

Implemented data reconciliation scripts to validate transaction accuracy for loyalty and refunds datasets.

Developed Java-based MapReduce batch workflows to support distributed processing in large-scale financial data analytics.

Managed version control with GitHub, implemented CI/CD pipelines for dbt model validation and automated deployment.

Collaborated with QA teams on data validation and production support for ETL applications across UAT and PROD environments.

Education Details:

Master of Information Technology from Swinburne University of Technology, Australia – 2025.

Bachelor of Computer Application from Veer Narmad South Gujarat University, India – 2018.

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