Data Engineer Power Bi

Mahanth Sai S

************@*****.*** +1-937-***-**** LinkedIn USA

PROFESSIONAL SUMMARY

Data Engineer with around 5 years of experience building data-intensive applications across healthcare, finance, and telecom. Skilled in Python, SQL (T-SQL/PL-SQL), PySpark, Hadoop, and ETL/ELT pipelines, with deep expertise in RDBMS (SQL Server, Oracle) and Big Data platforms. Proficient in data modeling, BI tools

(MicroStrategy, Tableau, Power BI, SSRS, SSAS), SSIS, CI/CD, and test automation. Experienced in financial risk, compliance, and regulatory reporting, transferable to banking and mortgage analytics. Adept in Agile delivery using JIRA and version control with GitHub. Strong background in unit testing, regression validation, and performance tuning to ensure reliable and auditable data workflows. Demonstrated success in partnering with cross-functional teams to deliver secure, scalable, and compliance-ready solutions that support business growth. SKILLS

Category Key Skills

Data Engineering & Pipelines ETL/ELT Design, Apache Airflow, Apache NiFi, dbt, Data Pipeline Orchestration, Workflow Automation, Batch, Streaming Data Processing, Regulatory Reporting Pipelines

Big Data Technologies Apache Spark, PySpark, Hadoop, Hive, Kafka, HDFS, Delta Lake, Hudi, Iceberg Cloud Platforms AWS (S3, Redshift, Glue, Lambda), Azure (Data Factory, Blob Storage, SQL DB, Synapse) Programming Languages Python (Pandas, NumPy, PySpark,) SQL (T-SQL, PL/SQL), Scala, Bash/Shell, JSON, YAML Data Modeling & Warehousing Dimensional Modeling (Star, Snowflake), Data Vault, OLTP/OLAP, Snowflake, Redshift, BigQuery, SQL Server, Data Marts, ODS, Normalization/Denormalization

Databases & Tools PostgreSQL, Oracle, MySQL, SQL Server, MongoDB, Cassandra, DB2, NoSQL, Relational & Columnar DBs, Performance Tuning, Indexing, JIRA, Agile, Scrum

DevOps & CI/CD Git, GitHub Actions, Jenkins, Terraform, Docker, Kubernetes, Airflow DAGs Regulatory & Compliance HIPAA Compliance, Data Privacy Regulations, Audit Trail Implementation, Risk Analytics, Fraud Detection Systems, Data Lineage, Data Catalogs, Data Profiling

Reporting & Visualization Power BI, MicroStrategy, Tableau, Looker, Superset, DAX, Power Query, SSRS, SSAS OLAP Cubes, Redash, Streamlit API & Integration RESTful APIs, JSON, API Data Ingestion, Webhooks, Power BI REST API, Azure Functions Monitoring & Logging CloudWatch, Datadog, Prometheus, ELK Stack, Logging Pipelines, Alerting Systems Statistical & Analytical Tools Descriptive Statistics, Trend Analysis, Hypothesis Testing, ANOVA, Regression, SciPy, Basic ML PROFESSIONAL EXPERIENCE

CVS HEALTH, PROVIDENCE, RI AUG 2023 - PRESENT

Data Engineer

• Designed and optimized Azure Data Factory & SSIS pipelines with Python and T-SQL to ingest, transform, and integrate regulated healthcare and financial datasets, ensuring compliance and audit readiness.

• Developed OLAP star schemas and SSAS OLAP cubes in Azure Synapse, enabling regulatory reporting and supporting mortgage-style risk and credit analytics.

• Built Power BI and SSRS dashboards for compliance officers, improving self-service analytics and accelerating decision-making across 50+ teams.

• Automated unit, integration, and regression testing with Python, reducing data errors by 25% and aligning with Bank of America’s test engineering practices.

• Implemented CI/CD pipelines with GitHub and Azure DevOps, reducing deployment effort by 50% while maintaining audit trails.

• Collaborated in Agile sprints using JIRA with risk analysts and compliance officers to refine requirements and deliver solutions aligned to regulatory needs. Environment: Azure Cloud Platform, SQL Server 2019/2022, Azure Data Factory, Azure Synapse Analytics, Azure SQL Database, SSIS 2022, Power BI, SSRS, Python, T- SQL, Databricks, Azure DevOps, Git, Visual Studio 2022, SQL Server Management Studio, Azure Data Studio, PowerShell, JSON/XML, REST APIs SUTHERLAND GLOBAL SERVICES, New York, USA SEP 2021 - DEC 2022 Data Engineer

• Built batch and streaming pipelines using AWS Glue, EMR (PySpark), Kinesis, and Airflow to process 10TB+ financial data for fraud detection, mortgage risk reporting, and regulatory compliance.

• Modeled financial data marts in Redshift with dimensional design (star/snowflake), improving regulatory reporting queries by 25%.

• Delivered real-time credit risk dashboards using MicroStrategy and Quicksight, enabling faster loan and risk decisions for 200+ analysts.

• Secured pipelines with IAM, KMS, and SFTP, ensuring compliance with SOX, GDPR, and mortgage data security standards.

• Automated CI/CD with GitHub & AWS CodePipeline, reducing manual deployment effort by 50% while maintaining compliance controls.

• Conducted proof-of-concepts (POCs) on new ingestion approaches (flat files, APIs) to mitigate integration risks and improve scalability. Environment: AWS Cloud Platform, Amazon S3, Amazon Redshift, AWS Glue, Amazon EMR (Spark 3.1.2), AWS Lambda, Amazon Kinesis, DynamoDB, Apache Airflow 2.3, PySpark 3.1, Python 3.8, SQL, Apache Kafka, AWS Step Functions, Amazon QuickSight, Amazon Athena, AWS CodePipeline, AWS CodeCommit, Git, IAM, AWS KMS, PostgreSQL, Linux/Unix, Bash scripting, JSON, Parquet, AWS CloudWatch BSNL, HYDERABAD, INDIA OCT 2020 – AUG 2021

Junior Data Engineer

• Developed 20+ ETL workflows in AWS Glue to transform telecom financial and operational data into Redshift, cutting processing time by 40% and enabling regulatory and mortgage-style reporting.

• Delivered Quicksight dashboards and Athena SQL reports for financial risk and compliance analytics, reducing reporting turnaround from days to minutes.

• Migrated 100+ SQL workloads to serverless AWS architectures, lowering costs by 30% while improving security and audit trail logging.

• Improved pipeline uptime to 99.9% with CloudWatch monitoring, alerting, and regression test validations.

• Refactored ETL pipelines with PySpark and modular workflows, improving performance by 35% and ensuring data lineage and compliance.

• Partnered with finance, risk, and IT teams to gather requirements and deliver solutions supporting compliance and operational analytics. Environment: AWS Cloud Platform, AWS Glue, Amazon S3, Amazon Redshift, Amazon QuickSight, Amazon Athena, AWS Lambda, Python 3.7, SQL, PySpark 2.4, Apache Spark, AWS CloudWatch, AWS IAM, PostgreSQL, MySQL, Linux, Bash, JSON, CSV, Parquet, Git, Jupyter Notebooks, AWS CLI CERTIFICATIONS & TRAINING

• AWS Certified Data Engineer Associate (In Progress)

• Microsoft Azure Data Engineer Associate (In Progress)

• Agile/Scrum Methodology Certified

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