Data Engineer Power Bi

***************@*****.***

RUDRANSHI DAVE

Data Engineer

ArlingtonTX +1-682-***-****

Results-oriented Data & Business Intelligence Engineer with over 3 years of experience architecting, implementing, and optimizing scalable data solutions across AWS, Azure, and GCP. Specialized in building real-time, high-performance ETL pipelines using Apache Spark, Airflow, and Kinesis, with a strong emphasis on data quality, reliability, and automation. Skilled at transforming complex datasets into actionable insights through advanced Power BI and Tableau dashboards. Proficient in managing the entire data lifecycle and passionate about delivering data-driven solutions that drive business impact and operational efficiency.

Summary

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Engineered and automated secure, cross-cloud ETL workflows utilizing Azure Data Factory and complex Apache Airflow DAGs to manage the bulk loading of billions of 401k and HSA transaction records from legacy Oracle systems and AWS RDS into a centralized Snowflake data warehouse.

Optimized Snowflake performance through the implementation of table clustering and partitioning, achieving a 35% improvement in query speeds. Published production-grade data models using advanced SQL and stored procedures to establish a 'Data as a Service' (DaaS) layer, reducing ad-hoc data requests by 40%. Designed and integrated a data quality and anomaly detection framework within Airflow pipelines combined with Jenkins CI/CD processes, decreasing migration-related data issues by 25% and ensuring downstream system reliability. Created and deployed over 15 interactive, client-facing Power BI dashboards to replace Fidelity’s legacy Oracle PSW frontend, enabling direct visualization of investment data and key performance indicators (KPIs). Conducted end-to-end management of ETL processes across cloud environments to facilitate seamless integration between legacy systems and modern platforms.

Enabled efficient orchestration of diverse datasets by designing scalable workflows for billions of transaction records using cutting-edge tools like Apache Airflow.

Spearheaded the development of advanced SQL-based solutions in Snowflake to support robust reporting layers tailored for business requirements.

Ensured high availability and integrity across critical analytics systems by implementing proactive monitoring mechanisms within automated pipelines.

Improved user experience for clients through enhanced reporting capabilities, providing visually engaging insights via Power BI dashboards linked with real-time investment metrics. Contributed effectively to Fidelity’s strategic technological transformation initiatives by streamlining operations with innovative data engineering practices leveraging Azure, AWS, Snowflake, Jenkins, Apache Airflow, and Power BI.

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Developed a scalable data pipeline for an Order Management System to process, track, and analyze real-time customer order and shipment data, improving supply chain visibility and operational efficiency. Designed and implemented real-time data streaming solutions using AWS Kinesis and Kafka to ingest high-volume transactional data, ensuring instant synchronization of updates for order fulfillment and shipment statuses. Built robust ETL processes with AWS Glue to transform raw data from multiple sources into structured, query-ready formats, enabling advanced analytics and comprehensive reporting. Engineered optimized database schemas in Oracle and Cassandra, supporting large-scale transactional data needs while ensuring high availability and efficient query performance. Established an event-driven architecture leveraging AWS SQS and SNS to facilitate reliable asynchronous communication between microservices and other system components. Aggregated and processed raw data using core data engineering principles, enabling the delivery of key operational metrics via real-time dashboards with actionable insights. Automated database maintenance tasks in Oracle, including backup processes and index optimizations, enhancing system performance while preserving data integrity. Work experience

Fidelity Investments, Westlake, TX 2025-07 present Data Engineer

Oracle,Hyderabad, India 201*-**-****-**

Data Engineer

Enhanced system reliability by implementing scalable architectures for mission-critical applications serving diverse business needs.

Ensured seamless integration of advanced analytics by designing workflows that bridged disparate systems into cohesive operations frameworks.

Streamlined the tracking of customer orders through innovative end-to-end solutions tailored to improve both operational efficiency and the customer experience.

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Education

University of Texas at Arlington, Arlington, TX 202*-**-****-** Master of Science in Computer Science

Skills

Strong Sense of Ownership Python

Data Warehouse SQL

Languages: Python, PySpark, SQL, R, Java

Libraries/Tools: Pandas, NumPy, OpenCV, scikit-learn, XGBoost, matplotlib, seaborn, Plotly, ggplot, DVC, MLflow Data Warehouses: Snowflake, Redshift, BigQuery

Data Engineering & ETL: Apache Airflow, Apache Spark, Hadoop, Databricks, SQL-based data modeling Databases: Oracle, PostgreSQL, SQL Server, DynamoDB, MongoDB BI & Visualization: Power BI (DAX), Tableau, Excel, Semantic Layer Modeling DevOps/MLOps: Jenkins, Docker, MLflow, DVC

Azure: Azure Data Factory

GCP: BigQuery, Cloud Functions, Vertex AI

AWS: S3, EC2, RDS, Kinesis, Lambda, SageMaker, Glue, Athena, Redshift Core Competencies: Strong Sense of Ownership, Attention to Details, Process improvement, Active listening, Problem- solving, Accountability, Big Picture Thinking.

Competence

Engineered a real-time, end-to-end data pipeline to ingest, process, and analyze streaming IoT data from vehicle sensors, containerizing the entire environment using Docker and Docker Compose for reproducibility and scalability. Implemented a distributed messaging system using Kafka for high-throughput data ingestion and utilized Spark Structured Streaming to perform real-time ETL operations, transforming raw JSON streams into structured Parquet files stored in an AWS S3 data lake.

Enabled scalable analytics by building an automated data catalog with AWS Glue, allowing for ad-hoc querying via Amazon Athena and integration with Amazon Redshift for complex data warehousing and business intelligence tasks. Projects

End-to-End Streaming ETL Pipeline for Smart City IOT Data Python, Apache Spark, Kafka, AWS (S3, Glue, Athena, Redshift), Docker, SQL, Parquet

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