Senior Data Engineer with ETL and BI Expertise

ANUSHA MARIYALA

Phone: 757-***-****

Email: ******.***********@*****.***

PROFESSIONAL SUMMARY:

Over 5 years of progressive experience delivering end-to-end solutions, specializing in building scalable data pipelines and automated workflows across enterprise and healthcare environments.

Deep expertise in Microsoft SQL Server, including database design, complex query development, indexing strategies, and advanced performance tuning for high-volume data systems.

Extensive hands-on experience developing enterprise ETL/ELT pipelines using SSIS, Azure Data Factory, and AWS Glue, enabling smooth ingestion, transformation, and processing of structured and unstructured datasets.

Proficient in cloud data warehousing technologies such as Snowflake, Azure Datawarehouse, and AWS Athena, supporting large-scale analytics and reporting initiatives.

Strong capability in writing optimized SQL code, stored procedures, functions, views, and performance-optimized queries for mission-critical business processes.

Demonstrated expertise in managing cloud storage solutions using Azure Data Lake and Azure Blob Storage, enabling efficient handling of massive datasets and cost-optimized storage strategies.

Advanced experience building business intelligence dashboards and analytical reports using Power BI, Tableau, and SSRS, empowering users with actionable and interactive insights.

Hands-on proficiency developing automated data processing workflows using Python, enabling seamless integration between ETL layers, APIs, and data platforms.

Strong understanding of dimensional modeling principles, including star schemas, snowflake schemas, and surrogate key strategies used in enterprise BI and analytics.

Skilled in diagnosing and optimizing SQL workloads using SQL Profiler, execution plans, and system-level monitoring to maximize database performance.

Experienced in orchestrating, scheduling, and monitoring ETL processes through Integration Services Catalog and other workflow automation platforms.

Proven ability to integrate complex data sources across multiple systems using SSIS, ADF, and AWS Glue, establishing robust data pipelines for analytics and reporting.

Strong experience with enterprise-grade DevOps practices using Git and TFS, including branching strategies, version control, and automated deployment methodologies.

Regularly collaborate with data science teams to prepare enriched, cleansed, and high-quality datasets required for machine learning, statistical modeling, and predictive analytics.

Skilled at designing highly interactive dashboards using Power BI and Tableau, enabling business stakeholders to track KPIs, identify trends, and make informed decisions.

Expert in developing advanced SSRS reporting solutions, including drill-down, drill-through, sub-reports, linked reports, and parameterized reporting architectures.

Practical experience using Elastic Search, Logstash, and Kibana to build search-driven analytics, log monitoring systems, and operational intelligence dashboards.

Extensive exposure to agile development, release management, and continuous integration pipelines using Git and TFS, ensuring fast and reliable delivery.

Proficient in configuring and optimizing ADF Pipelines, Snowflake Tasks, and complex orchestration flows to support automated ELT workloads.

Demonstrated ability to optimize ETL performance by redesigning data flows, improving parallelism, and reducing end-to-end pipeline latency.

Skilled in gathering, analyzing, and transforming business requirements into scalable, secure, and high-performance data engineering solutions.

Experienced in performing data quality checks, data validation, reconciliation processes, and implementing standards to maintain accuracy and consistency.

Highly proficient using SSMS and Visual Studio for database development, debugging, code reviews, and deployment workflows.

Strong problem-solving capabilities with a proven track record of troubleshooting issues across ingestion, transformation, storage, and analytics layers.

Skilled at maintaining detailed technical documentation, data flow diagrams, and architecture artifacts to support cross-functional collaboration.

Adept at working in fast-paced Agile environments using Jira, consistently delivering high-quality data pipelines, reports, and analytics solutions within sprint timelines.

Hands-on experience leveraging Snowflake AI capabilities, including Cortex-powered SQL functions, search optimization, and intelligent data transformations to enhance analytics and reporting workflows.

Experienced in preparing, enriching, and optimizing datasets in Snowflake to support AI-driven analytics, automated insights, and downstream machine learning use cases.

TECHNICAL SKILLS:

Databases & Warehousing

Microsoft SQL Server, Azure Datawarehouse, Snowflake(including Snowflake AI / Cortex), AWS Athena, Azure SQL Databases

ETL / Data Integration

SSIS, Azure Data Factory (ADF), AWS Glue, Integration Services Catalog, SQL Profiler

Cloud Platforms & Storage

Azure Data Lake, Azure Blob Storage, AWS Glue Services, ADF Pipelines

Business Intelligence & Reporting

Power BI, Tableau, SSRS, SSAS

Programming & Scripting

Python, T-SQL, SQL, Jupyter, Spyder

Search, Logging & Monitoring

Elastic Search, Logstash, Kibana

Development Tools & IDEs

SSMS, Visual Studio, SSDT, BIDS

Version Control & Agile Tools

Git, TFS, Jira

Other Expertise

Data Modeling (Star/Snowflake), Performance Tuning, Stored Procedures, Indexing, ETL Automation, Workflow Orchestration

EDUCATION:

Bachelor’s in information technology, Jawaharlal Nehru Technological University (JNTU), Hyderabad, India

PROFESSIONAL EXPERIENCE:

Eyecare Partners LLC, MO July 2022 – Present

Role: Data Analyst

Responsibilities:

Designed and implemented cloud-native data ingestion pipelines using Azure Data Factory (ADF) to integrate clinical, operational, and patient-care datasets from multiple systems.

Built scalable data lake architectures using Azure Data Lake and Azure Blob Storage, supporting structured and unstructured healthcare data.

Developed advanced ELT processes using Azure SQL, Azure Data Warehouse, and Snowflake to prepare curated datasets for analytics.

Created complex T-SQL stored procedures, functions, and views to support KPI dashboards, regulatory reporting, and operational analytics.

Engineered metadata-driven ADF pipelines using dynamic expressions, parameters, and reusable templates for multi-source ingestion.

Implemented automated quality checks and reconciliation frameworks using Python and ADF to ensure data accuracy and reliability.

Built end-to-end workflows to support clinical outcomes tracking using curated datasets stored in Azure SQL and Snowflake.

Developed enterprise dashboards using Power BI, enabling executives and clinical teams to monitor patient metrics and operational performance.

Designed data models using star and snowflake schemas to improve analytical query performance and reporting usability.

Integrated API-based data sources into the analytics ecosystem using ADF, Python, and REST connectors.

Set up secure access, RBAC policies, and data governance controls across Azure Data Lake, SQL pools, and reporting layers.

Built automated monitoring dashboards using Power BI and custom tables to track data pipeline health and data freshness.

Migrated legacy ETL workloads to Azure Data Factory, significantly improving scalability and reducing operational costs.

Collaborated with data science teams to prepare feature-rich datasets for predictive healthcare models using SQL and Python.

Implemented CDC (Change Data Capture) strategies in ADF and Azure SQL to support incremental data loading and high-volume processing.

Configured ADF Integration Runtimes and optimized compute settings to improve pipeline throughput and reduce processing times.

Created reusable validation scripts using Python and T-SQL to automate testing of new data sources and ingestion paths.

Partnered with business stakeholders to translate healthcare reporting requirements into scalable Azure-based data solutions.

Conducted root-cause analysis on failed data loads using Azure Monitor, SQL Profiler, and custom error-handling frameworks.

Built deployment-ready CI/CD pipelines for ADF assets using Git, ensuring efficient version control and environment migrations.

Automated daily operational workflows, reducing manual intervention using ADF triggers, PowerShell, and Python.

Improved the reliability of high-volume data processes by optimizing ADF Mapping Data Flows and SQL transformations.

Conducted performance benchmarking on Azure SQL queries and optimized indexes, partitions, and execution plans.

Created detailed documentation including data flow diagrams, pipeline logic, mapping sheets, and architecture specifications.

Provided ongoing support, enhancements, and troubleshooting for all Azure Data Factory, Power BI, and SQL-based analytics solutions.

Leveraged Snowflake AI (Cortex) features to enhance SQL-based analytics, enabling faster insights through intelligent data transformations and semantic enrichment.

Designed AI-ready datasets in Snowflake by optimizing schemas, metadata, and data quality to support advanced analytics and predictive modeling initiatives.

Implemented search optimization and performance tuning strategies in Snowflake to improve query efficiency for large analytical workloads.

Collaborated with analytics and data science teams to prepare curated Snowflake datasets used in AI-driven reporting and decision support.

Environment: Azure Data Factory (ADF), Azure Data Lake, Azure Blob Storage, Azure SQL, Azure Datawarehouse, Snowflake, Power BI, Python, T-SQL, SQL Server, SQL Profiler, ADF Mapping Data Flows, Integration Runtimes, PowerShell, Git, Jira, Visual Studio, SSMS, Azure Monitor, ETL/ELT Frameworks, Data Modeling (Star/Snowflake)

Reflect Health, Mason, OH May 2020 – June 2022

Role: SQL Developer

Responsibilities:

Designed and built scalable cloud-based data pipelines using Azure Data Factory (ADF) to ingest data from on-prem, APIs, and third-party sources into Azure Data Lake.

Developed robust ELT frameworks leveraging Azure Data Warehouse, Azure SQL, and Snowflake to support enterprise analytics.

Created complex T-SQL stored procedures, views, and tables to transform raw data into curated datasets for reporting and data science teams.

Implemented ingestion workflows for structured, semi-structured, and unstructured data stored in Azure Blob Storage and Azure Data Lake.

Built automated data transformations using ADF Mapping Data Flows, SSIS, and Python, reducing manual effort and improving pipeline reliability.

Developed reusable data models and fact/dimension structures based on star and snowflake schemas for BI and analytics workloads.

Optimized ETL/ELT performance by tuning SQL queries, parallelizing ADF pipelines, and improving index strategies in Azure SQL.

Created interactive dashboards and metrics using Power BI, enabling leadership to track clinical outcomes and business KPIs.

Collaborated with cross-functional business teams to convert requirements into scalable Azure data solutions.

Built parameterized and dynamic pipelines in ADF to handle multi-tenant data loads with minimal code duplication.

Configured and monitored ADF triggers, integration runtimes, and job performance to ensure reliable daily pipeline execution.

Implemented data validation and reconciliation processes using SQL, ADF, and Python to ensure end-to-end data accuracy.

Automated operational workflows using PowerShell, Python, and ADF, reducing processing time and manual interventions.

Migrated legacy SSIS pipelines into Azure Data Factory, improving scalability and cloud-native performance.

Created Power BI datasets, measures, and dataflows optimized for large-scale reporting consumption.

Set up RBAC and security controls within Azure to manage access to data lakes, SQL pools, and sensitive healthcare data.

Developed auditing and logging frameworks using Azure Monitor and custom logging tables to track pipeline activities.

Collaborated with DevOps teams to deploy data pipelines and ADF assets using Git and CI/CD pipelines.

Conducted root-cause analysis for pipeline failures using SQL Profiler, Azure Monitor, and detailed log tracing.

Worked closely with application teams to design and integrate new data sources into Azure Data Lake and analytics layers.

Built automated alerts and health checks for ADF pipelines to ensure proactive monitoring and minimize downtime.

Created and maintained technical documentation, architecture diagrams, data mappings, and operational guides for Azure-based solutions.

Improved system reliability and performance through continuous optimization of ADF, Azure SQL, Power BI, and ETL jobs.

Environment: Azure Data Factory (ADF), Azure Data Lake, Azure Blob Storage, Azure SQL, Azure Datawarehouse, Power BI, Snowflake, SSIS, SQL Server, T-SQL, Python, PowerShell, Git, Jira, Visual Studio, SSMS, Azure Monitor, Data Modeling (Star/Snowflake), ETL/ELT Pipelines

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