Power Bi Azure Data

Sandeep Babu

Sr.PowerBI Developer

828-***-**** *********@*****.***

Professional Summary:

Seasoned Power BI Developer with over 6+ years of experience in translating complex data into actionable insights through interactive, enterprise-grade dashboards and reports. Expert in Power BI Desktop & Service, data modeling (star schemas), DAX, and Power Query (M), with a strong foundation in Azure Synapse Analytics, Azure Data Factory, and SQL Server/Azure SQL. Proven track record of reducing report-generation time by up to 50% through optimized data models and visuals, implementing row-level security and governance to safeguard sensitive information, and automating dataset refresh pipelines via Power BI Dataflows and Azure DevOps for near-real-time analytics. Adept at collaborating with business stakeholders to define requirements and deliver compelling visual narratives that drive informed decision-making.

Technical Skills:

Cloud Platforms & Services: AWS EC2, Glue, Lambda, Redshift, CloudFormation, CloudWatch, S3, Step Functions, EMR, RDS, IAM, Google Cloud (Pub/Sub, Dataflow, BigQuery, Storage), Azure Data Lake, Data Factory, Databricks, Synapse, Active Directory, Key Vault, DevOps

Big Data & Distributed Systems: Hadoop, HDFS, Apache Spark, Kafka, Sqoop, Airflow, YARN, MapReduce, Hive, HBase, Ranger, Spark SQL, Spark Streaming

Programming Languages & Frameworks: Python, PySpark, NumPy, Pandas, TensorFlow, PyTorch, Scikit-learn, Shell Scripting, JavaScript, Java

Data Engineering & ETL Tools: ETL Pipelines (Batch/Real-time), AWS Glue, Talend, SSIS, Azure Data Factory, Databricks, Sqoop, Hive, Data Transformation & Validation, Ingestion (Kafka, Lambda)

Databases & Data Warehousing: MongoDB, Cassandra, Snowflake, PostgreSQL, MySQL, SQL Server, Oracle DB, RDS, Redshift, BigQuery, Data Modelling

Data Visualization & Reporting: Tableau, Power BI, Grafana

DevOps & Automation Tools: Terraform, Ansible, Jenkins, Docker, Kubernetes, CloudFormation, Azure DevOps

Data Security & Compliance: Data Security, Encryption, Compliance (GDPR, HIPAA), IAM, Azure Key Vault, Ranger, OAuth 2.0, Data Integrity

APIs & Integration: RESTful APIs, API Development, Serverless API (AWS Lambda), OAuth 2.0 Authentication

Operating Systems: Linux, Windows Server, macOS

Monitoring & Troubleshooting: Splunk, Grafana, New Relic, CloudWatch, ServiceNow, Bugzilla, Jira, Azure Monitor

Version Control & Collaboration: Git/GitHub, GitLab, Branching, Merging

Project Management & Methodologies: Agile, Scrum, Kanban, Cross-functional Collaboration, IT Service Management (ServiceNow).

Work Experience:

City National Bank of Florida, Coral Gables, FL Aug 2023 to Present

Sr. PowerBI Developer

Roles & Responsibilities:

Delivered end-to-end business intelligence solutions for media content operations using Microsoft Azure technologies including Azure Data Lake, Azure Databricks, Azure Data Factory (ADF), Azure SQL Data Warehouse, and Azure Synapse.

Built unified data experiences using Microsoft Fabric by integrating Lakehouses (OneLake), Power BI, and Synapse artifacts under a single analytics workspace.

Designed and deployed 12 interactive Power BI dashboards for equity trading analytics, reducing manual report generation by 50%

Architected star-schema data models in Power BI Desktop to support high-volume trade and risk data set.

Optimized 25+ complex DAX measures and calculated columns, improving report query performance by 40%

Implemented dynamic row-level security for 300+ users, ensuring data access compliance across regional trading desks

Built and scheduled Power BI Dataflows in Azure Data Factory to ingest and transform 20 GB of nightly market data

Configured incremental refresh policies, reducing dataset refresh time from 4 hours to under 1 hour

Developed custom Power BI visuals using TypeScript and D3.js to highlight trading anomalies and trends

Integrated Power BI Service with Azure Synapse Analytics to enable direct-query reporting on real-time data

Automated deployment of Power BI artifacts via Azure DevOps pipelines, enforcing version control and CI/CD best practices

Established sensitivity labels and workspace permission structures to comply with GDPR and internal security standards

Created paginated reports in Power BI Report Builder for regulatory submissions, streamlining audit preparedness

Conducted performance tuning sessions—indexing source tables and optimizing DAX—to reduce dashboard load times by 30%

Collaborated with traders and business analysts to gather requirements, wireframe report layouts, and iterate on prototype dashboards

Authored end-user training materials and led 5 hands-on workshops, increasing self-service BI adoption by 35%

Monitored usage metrics via Power BI audit logs and lineage view, identifying under-utilized reports and driving continuous improvement.

Ingested high-volume viewership and content metadata into Azure Delta Lake and processed it in Azure Databricks for downstream analytics.

Designed media-specific ELT data pipelines using Airflow, Python, DBT, Stitch Data, and GCP to support audience measurement and cross-platform engagement insights.

Used Azure Data Fabric Pipelines to orchestrate ingestion of structured/unstructured content logs into Fabric Lakehouses, enabling near real-time analytics.

Utilized Python and SQL to automate repetitive media data ingestion, improving speed and reducing human errors in the data curation process.

Delivered PySpark-based ETL pipelines on Azure Databricks, transforming raw media and subscriber data, orchestrated via ADF and scheduled through Azure Automation & Tidal Scheduler.

Performed data analysis using Python, R, SQL, Hive, Spark SQL, and Excel to support decisions in program scheduling, campaign targeting, and subscriber churn prediction.

Participated in agile team sprints with a test-driven approach to build reliable Python-based ETL routines for viewership and platform performance metrics.

Developed multi-stage PySpark transformation pipelines in Databricks over Delta Lake, optimizing runtime and reducing costs for batch processing of OTT logs.

Integrated Fabric Notebooks for exploration and model prototyping on top of unified datasets sourced from Azure SQL, ADLS Gen2, and external APIs.

Implemented authentication and rate-limiting policies in Azure APIM for REST APIs delivering content metadata and viewership metrics from Snowflake and Power BI.

Utilized Databricks SQL for real-time querying of user behavior data by analysts supporting risk mitigation and content investment decisions.

Built Power BI dashboards for executive-level reporting on ad performance, streaming trends, and compliance with broadcasting standards.

Authored stored procedures and dynamic SQL in Snowflake to automate compliance and financial reporting for content distribution and royalty tracking.

Environment: Azure Databricks, Data Factory (ADF), Data Lake Formation, Data Zone, Data Warehouse, Logic Apps, PostgreSQL, Python, Talend, Functional App, Snowflake, MS SQL, Oracle, MS SQL Server 2016, ETL, SSIS, SSRS, Cassandra, Oracle 12c, Oracle Enterprise Linux, Teradata, Jenkins, Power BI, Git, CI/CD, Unix Shell Scripting

Change Healthcare, Fort Worth, Texas Aug 2021 to Aug 2023

PowerBI Developer

Roles & Responsibilities:

Extensive hands-on experience of writing notebooks in data bricks using python/Spark SQL for complex data aggregations, transformations, schema operations

Built Power BI dashboards for executive-level reporting on ad performance, streaming trends, and compliance with broadcasting standards

Delivered optimized Gold Layer datasets to downstream BI tools such as Power BI and Snowflake, enabling faster and more accurate healthcare insights

Designed and automated scalable ETL workflows using Azure Data Factory and Azure Databricks to prepare clinical, claims, and supply chain data for Power BI reporting

Secured sensitive healthcare data connections in Power BI by integrating Azure Active Directory and Azure Key Vault for HIPAA-compliant authentication

Established data governance and lineage tracking with Azure Purview to enhance trust in Power BI reports and meet regulatory requirements

Integrated Power BI datasets with Azure Data Lake Storage and Azure Synapse for direct-query access to real-time operational metrics

Integrated clinical REST APIs delivering critical operational metrics into Power BI for unified reporting

Optimized Power BI data models using star-schema design techniques and advanced DAX measures for efficient query performance

Automated dataset refresh schedules in Power BI Service via Azure Data Factory pipelines to maintain near-real-time insights

Developed paginated reports in Power BI Report Builder for regulatory compliance and audit-ready documentation

Leveraged Azure DevOps pipelines to automate deployment of Power BI reports, datasets, and dataflows across development and production workspaces

Monitored Power BI usage and performance via audit logs to identify under-utilized reports and optimize dashboard performance

Collaborated with clinical and operational teams using JIRA to gather requirements, prototype Power BI dashboards, and iterate on analytics solutions

Configured row-level security (RLS) in Power BI Service to enforce user-specific data access controls, ensuring sensitive information is protected

Delivered curated Databricks Gold-Layer models to support advanced Power BI analytics on healthcare data

Conducted performance benchmarking across Databricks and Azure Synapse to identify optimal environments for complex SQL workloads.

Automated infrastructure as code (IaC) deployments using Terraform to ensure consistent cloud resource provisioning across staging and production.

Established data governance controls at the Gold Layer, leveraging Azure Purview for lineage tracking and compliance validation under HIPAA guidelines.

Enabled data discoverability and reuse by registering Fabric assets into Microsoft Purview, supporting lineage and compliance tracking.

Developed Data pipelines using python for medical image pre-processing, Training and Testing.

Led DevOps integration efforts between Azure DevOps and Git repositories to establish CI/CD pipelines for Spark-based jobs.

Connected DBT to Azure SQL Database / Azure Synapse using ODBC/SQL endpoints for transformation tasks and model execution.

Delivered optimized Gold Layer datasets to downstream BI tools such as Power BI and Snowflake, enabling faster and more accurate healthcare insights.

Implemented JavaScript scripts in Azure Function Apps for on-the-fly data cleansing and transformation tasks before data reached Databricks pipelines.

Partnered with clinical data teams to validate and normalize health record formats, improving data accuracy across hospital networks.

Implemented end-to-end data lineage and auditing using Apache Ranger and Azure Purview to meet regulatory and operational traceability.

Developed custom Python modules to extend ADF capabilities, enabling advanced data manipulation and integration functionalities not natively supported by ADF.

Utilized Azure Key Vault to securely manage and access DBT credentials and environment-specific variables.

Scheduled and orchestrated Databricks jobs using Databricks Workflows and integrated with CI/CD tools for automation.

Wrote complex SQL queries in Snowflake to aggregate clinical and operational data, providing insights into medication adherence and supply chain anomalies.

Utilized SQL-based transformations in Databricks Delta Live Tables to automate schema enforcement and data quality checks.

Environment: Azure Data Factory, Azure Databricks, Apache Spark, Apache Kafka, Azure Data Lake, Snowflake, TensorFlow, Azure ML, PySpark, Cassandra, Google BigQuery, Azure AD, Azure Key Vault, DBT, Terraform, Power BI, Apache Ranger.

Lowe's, Mooresville, NC Dec 2019-August 2021

Data Engineer

Roles & Responsibilities:

Developed and optimized ETL pipelines using Google Cloud Pub/Sub, BigQuery, and Dataflow to handle real-time and batch processing of retail sales transactions, product inventory updates, and customer purchase behavior data across Lowe’s store network.

Automated Python-based data migration and validation workflows between PostgreSQL databases and cloud storage, ensuring timely updates to Lowe’s product catalogs and accurate inventory tracking for home improvement products.

Collaborated closely with data science teams to preprocess extensive customer and product datasets leveraging Pandas and NumPy, supporting machine learning initiatives for personalized product recommendations and dynamic pricing adjustments at Lowe’s.

Created automated data validation frameworks to maintain data accuracy and integrity across retail datasets, enabling reliable analytics for sales forecasting, inventory management, and product performance measurement.

Improved query performance in BigQuery and PostgreSQL by implementing Lowe’s-specific indexing and partitioning strategies, significantly enhancing responsiveness for high-volume sales and stock availability queries.

Implemented data modeling in Snowflake including star and snowflake schemas for efficient analytics processing.

Implemented Delta Live Tables for declarative ETL pipelines with data quality enforcement in Databricks.

Leveraged Apache Spark for distributed processing of customer loyalty program data, segmentation analysis, and store operational metrics, delivering scalable and efficient data transformations.

Developed Python AWS serverless lambda with concurrent and multi-threading to make the process faster and asynchronously executing the callable. And debug SQL queries.

Developed and deployed predictive models using TensorFlow and Scikit-learn to forecast demand patterns, minimize overstock/understock challenges, and optimize Lowe’s supply chain management.

Developed RESTful APIs using Java and JavaScript to support product availability and customer loyalty services across mobile and web applications.

Integrated Apache Sqoop and Informatica to transfer product and sales data from on-premises systems into cloud environments, accelerating reporting and analytics capabilities.

Applied Python to optimize data loading and processing performance in Power BI, resulting in faster report generation and improved user experience.

Engineered real-time streaming solutions with Google Cloud Pub/Sub to capture point-of-sale and customer interaction data across Lowe’s multiple retail channels, enabling timely marketing and operational insights.

Created interactive Tableau dashboards for merchandising and store operations teams, visualizing sales trends, inventory levels, and customer insights to support strategic decision-making.

Secured APIs using OAuth 2.0 protocols to protect sensitive Lowe’s product, sales, and customer data, ensuring compliance with enterprise security standards.

Automated provisioning of cloud infrastructure using Terraform and Infrastructure as Code (IaC) principles, facilitating efficient resource management during Lowe’s seasonal promotions and new store openings.

Environment: Google Cloud Pub/Sub, Big Query, Google Cloud Dataflow, Python, Databricks, PostgreSQL, Pandas, NumPy, Apache Spark, TensorFlow, Scikit-learn, RESTful APIs, Apache Sqoop, Snowflake, Informatica, Google Cloud, New Relic, OAuth 2.0, Tableau, Terraform, Google Analytics.

Citi Bank,Tampa, FL Oct 2018 to Dec 2019

Data Engineer

Roles & Responsibilities:

Optimized batch processing workflows by fine-tuning performance and scalability, reducing processing time, and ensuring system reliability under heavy data loads.

Applied Python, NumPy, and Scikit-learn to perform data transformation, validation, and predictive analytics, generating actionable insights to drive business strategy and operational efficiency.

Enhanced data security by implementing access control, encryption techniques, and compliance measures to safeguard sensitive information and meet data protection regulations.

Utilized Bugzilla to monitor and resolve issues within data pipelines, ensuring timely resolution and minimizing system downtime.

Deployed and maintained high-performance data storage solutions using Hive and HBase, efficiently managing structured and semi-structured data for optimized retrieval and processing.

Designed and managed scalable ETL pipelines with SSIS, extracting, transforming, and loading data from various sources, ensuring high-quality, and seamless data processing workflows.

Developed Spark/Scala, Python for regular expression (regex) project in the Hadoop/Hive environment with Linux/Windows for big data resources.

Implemented robust data security protocols and monitored compliance with industry regulations, ensuring secure and compliant data workflows.

Developed batch and real-time data processing solutions using Apache Spark, Spark Streaming, and HDFS to meet stringent low-latency requirements, optimizing workflow efficiency.

Engineered real-time data pipelines with Apache Kafka, enabling continuous data streaming and processing to support dynamic business needs and improve decision-making.

Leveraged advanced Excel functions, including PivotTables, VLOOKUP, and custom formulas, to analyse and visualize data, creating interactive dashboards and streamlining reporting processes.

Environment: Python, NumPy, Scikit-learn, Bugzilla, Hive, HBase, SSIS, Apache Spark, Spark Streaming, HDFS, Git, Apache Kafka, Excel (PivotTables, VLOOKUP), AWS.

Education: Masters of science in information science at Clemson University.

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