Data Engineer Power Bi

SHARMILA BOGADHI DATA ENGINEER

KS, USA +1-914-***-**** ****************@*****.*** LinkedIn AWS Snowflake Summary

Data Engineer with 3 years of experience delivering enterprise-grade solutions using AWS, Snowflake, Azure, and Databricks. Expert in designing scalable ETL pipelines, optimizing warehouse performance, and enabling actionable insights. Proven track record of building cost-efficient, reliable systems that enhance reporting accuracy, reduce operational delays, and accelerate decision-making across business teams. Technical Skills

Programming & Scripting: Python (Pandas, NumPy), SQL (joins, CTEs, window functions), Java (basic), Bash ETL & Data Engineering: Apache Spark (batch & streaming), Apache Airflow, SSIS, ETL/ELT pipeline design Databases: MySQL, PostgreSQL, MongoDB, AWS RDS

Cloud Platforms: AWS (S3, Redshift, Glue, Lambda, Kinesis, CloudWatch, IAM, Route 53, WAF), Azure (Data Factory, Synapse, Data Lake, Databricks), GCP (BigQuery, Dataflow, Pub/Sub, Cloud Storage) Big Data Tools: Apache Spark (PySpark), Hive, Hadoop (basic understanding) Data Warehousing & Modeling: Snowflake (clustering, time travel, data sharing, incremental loads, performance tuning), AWS Redshift, Dimensional Modeling, Star Schema, ER Diagrams

BI & Visualization: Power BI, Tableau, AWS QuickSight DevOps & CI/CD: Git, GitHub, GitLab, Jenkins, Docker, Kubernetes Workflow Orchestration: Apache Airflow, Cron Jobs

Development Tools: Jupyter Development Tools: Notebook, VS Code, Linux/Unix, Agile/Scrum Professional Experience

Data Engineer Centene Corporation Jan 2025 – Present

• Engineered Snowflake data models with clustering, incremental loads, and materialized views, improving Power BI dashboard performance by 65% and cutting report refresh time from hours to minutes.

• Designed and automated ETL pipelines in AWS Glue, Lambda, and S3 to integrate 12+ enterprise data sources, reducing processing time by 40% and accelerating analytics delivery across business units.

• Implemented PySpark-based quality checks for schema validation, duplicates, and nulls, preventing 50K+ monthly data issues and strengthening compliance reporting for claims, member, and provider datasets.

• Developed Airflow DAGs with SLA monitoring and failure alerts on AWS, achieving 99%+ job success rate and reducing operational downtime across critical healthcare reporting pipelines.

• Partnered with analysts to create Snowflake reporting layers integrated with Tableau and QuickSight, improving provider scoring and risk analysis adoption by 60% across 25+ enterprise teams.

• Monitored pipelines with CloudWatch, GitLab CI/CD, and Kubernetes-deployed services, ensuring scalable, cost-efficient infrastructure capable of handling increasing workloads without degrading performance. Data Engineer Larsen & Toubro (L&T) Jan 2022 – Jul 2023

• Designed and implemented Azure Data Factory and Databricks ETL pipelines processing 3TB+ daily manufacturing data, supporting procurement, engineering, and enterprise operations while improving data availability SLA compliance by 35%.

• Built Delta tables in Databricks for machine learning experiments, enabling predictive maintenance use cases that shortened decision cycles by 30%, enhanced workforce scheduling, and increased asset utilization by 22%.

• Optimized queries in Amazon Redshift and Azure Synapse with partitioning and distribution strategies, reducing dashboard refresh times by 75% and enhancing operational reporting speed for 200+ enterprise users.

• Developed a hybrid storage model integrating Azure Data Lake and AWS S3, archiving historical datasets while cutting monthly storage costs by 12% and maintaining immediate accessibility for analytics teams.

• Automated and monitored Airflow pipelines with SLA alerts, retry logic, and failure notifications, improving pipeline reliability from 90% to 99.5% and saving 20+ hours of manual intervention monthly.

• Collaborated with digital transformation teams to embed predictive analytics into construction scheduling, improving resource allocation efficiency and reducing project delays by 15% across EPC initiatives. Data Analyst Intex Technologies Jan 2021 – Dec 2021

• Developed advanced SQL queries to extract and transform sales, inventory, and returns metrics from MySQL databases, improving reporting accuracy and accelerating supply chain insights for 15+ regional leadership teams.

• Built interactive Power BI dashboards visualizing product sales, distributor performance, and stock levels, reducing manual preparation time by 30% while enhancing visibility into 10+ regional performance KPIs.

• Cleaned ERP export datasets from SAP systems, correcting missing values, duplicates, and integrity issues, which boosted finance and supply chain reporting accuracy by 25% across multiple business functions.

• Partnered with sales and marketing teams to analyze campaign effectiveness, compiling insights on conversion rates, customer engagement, and promotional ROI that informed regional go-to-market strategies.

• Documented workflows and SOPs for SQL extractions, validation checks, and recurring reporting, accelerating new analyst onboarding by 40% and ensuring consistent reporting practices across the team.

• Supported senior analysts with ad-hoc SQL data pulls, validations, and quarterly review decks, enabling leadership to make faster data-driven strategic decisions that improved sales planning and forecasting accuracy. Education

Master’s in Computer Science May 2025

University of Central Missouri, MO, USA

Bachelor of Technology (ECE) May 2023

Geethanjali College of Engineering & Technology, India Certification

AWS Certified Data Engineer – Associate Mar 2025

Snowflake's Certification SnowPro Core Aug 2025

Projects

Airlines Data Ingestion & Analytics Pipeline (Jan 2025)

• Built a streaming + batch ingestion system (AWS Kinesis, Glue, Redshift, S3) with <5 min latency.

• Created QuickSight dashboards with CloudWatch monitoring, enhancing SLA compliance by 40%. University Blog Hosting on AWS (May 2024)

• Deployed scalable blog platform (EC2, RDS, S3, CloudFront), achieving 99.9% uptime and handling 10K+ users/month.

• Integrated Route 53, IAM & WAF with CloudWatch logging, reducing incident triage time by 45%.

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