Sindhu B.

• Designed and implemented enterprise-scale data pipelines using Azure Data Factory, Azure Databricks, and Apache Spark to ingest high-volume OLTP data from operational inventory, order management, and fulfillment systems into an OLAP-optimized lakehouse architecture for analytics and reporting. • Implemented Change Data Capture (CDC) mechanisms to efficiently process incremental updates from transactional systems, reducing latency and ensuring accurate synchronization between OLTP sources and downstream analytical models. • Developed both batch and real-time streaming pipelines using Azure Data Factory, Event Hubs, Kafka, and Databricks Structured Streaming to ingest transactional data into Delta Lake, enabling low-latency analytics and seamless propagation to Snowflake and Azure Synapse. • Built and optimized cloud data warehouse and lakehouse architectures leveraging Azure Synapse, ADLS Gen2, Delta Lake, Unity Catalog, and Snowflake, enabling scalable, high-performance analytics for demand forecasting, supplier performance, inventory optimization, and order fulfillment KPIs. •Orchestrated complex workflows using Apache Airflow and Azure Data Factory triggers, managing job dependencies, SLA monitoring, incremental loads, Snowflake data synchronization, and fault-tolerant processing across Azure and warehouse environments. • Performed large-scale data transformations and analytics using Python, PySpark, and advanced SQL to enable inventory replenishment models, demand planning, order fill-rate analysis, vendor lead-time analytics analytics, and operational reporting. • Applied Infrastructure as Code (IaC) using Terraform and Azure Resource Manager (ARM) templates to provision secure, repeatable, and compliant Azure environments aligned with enterprise governance best practices.

• Designed and built enterprise-scale cloud data platforms on AWS, enabling scalable ingestion, processing, and consumption of insurance data across policy, claims, and financial domains. • Developed and maintained modular, reusable ETL/ELT frameworks using AWS Glue (PySpark), Lambda, and Kinesis, supporting both batch and streaming workloads within a standardized platform architecture. • Contributed to data platform engineering initiatives, including environment standardization, reusable pipeline templates, metadata-driven ingestion, and centralized orchestration patterns. • Architected and optimized data lake and warehouse layers using Amazon S3, Redshift, and Snowflake, ensuring high availability, performance, and cost efficiency for enterprise analytics workloads. • Implemented platform-level capabilities such as data partitioning strategies, schema evolution handling, CDC frameworks, and automated data quality validation. • Built and optimized data models (fact/dimension) to support actuarial analytics, financial reporting, and regulatory compliance across large-scale datasets. • Established CI/CD pipelines for data platform deployments using Terraform, CloudFormation, CodePipeline, and CodeBuild, enabling automated and consistent infrastructure and pipeline releases. • Strengthened platform observability and reliability by implementing monitoring, logging, and alerting frameworks using CloudWatch, CloudTrail, and SNS. • Orchestrated workflows using Apache Airflow and AWS Step Functions, enabling scalable, fault-tolerant pipeline execution across multiple domains. • Implemented enterprise-grade security and governance (IAM, KMS, VPC, RBAC), ensuring secure handling of sensitive insurance and financial data. • Collaborated with cross-functional teams to enable self-service data platform capabilities, improving data accessibility for analysts, data scientists, and business users.

• Led large-scale data integration initiatives across multiple healthcare systems, consolidating data from hospitals, labs, claims systems, and public health sources into unified cloud platforms. • Designed and implemented robust data integration pipelines using Google Cloud Dataflow, Apache Beam, and Pub/Sub to ingest, transform, and standardize diverse healthcare datasets. • Integrated heterogeneous data formats including HL7, FHIR, APIs, CSV extracts, and legacy database systems into centralized, analytics-ready data models. • Developed data harmonization and transformation frameworks to standardize clinical, claims, and operational datasets for province-wide reporting and analytics. • Executed end-to-end data migration and integration strategies from on-prem Hadoop and legacy systems to GCP (BigQuery, GCS, Dataproc), ensuring data consistency and integrity. • Built data validation, reconciliation, and quality frameworks to ensure accuracy and compliance of integrated healthcare datasets, including PHI-sensitive data. • Orchestrated integration workflows using Cloud Composer (Airflow), enabling reliable scheduling, dependency management, and monitoring of data pipelines. • Designed centralized data models and integration layers to support healthcare analytics use cases such as patient outcomes, funding allocation, wait times, and system performance. • Implemented secure data integration practices aligned with healthcare regulations, including encryption, IAM controls, audit logging, and privacy compliance. • Leveraged dbt for transformation and integration standardization, enabling reusable models, automated testing, and consistent data definitions across teams. • Enabled downstream analytics and reporting by delivering clean, integrated datasets for tools like Looker and Power BI.

• Designed, developed, and maintained scalable data pipelines using Apache Spark, Hadoop, and Azure Synapse Analytics to process high-volume financial transactions, payment records, loan data, and customer activity logs, enabling end-to-end visibility into operational and risk metrics. • Built and automated ETL workflows using Python, PySpark, and Scala to ingest, cleanse, and transform data from core banking systems, payment gateways, CRM platforms, and ERP systems into centralized Azure Data Lake environments, supporting real-time monitoring of transactions and financial operations. • Integrated and managed structured and unstructured datasets across SQL Server, PostgreSQL, MongoDB, and Cassandra, supporting use cases such as fraud detection, credit risk analysis, customer 360 views, regulatory reporting, and transaction reconciliation across enterprise financial systems. • Implemented CI/CD pipelines and DevOps practices using Jenkins, Git, and Maven, ensuring automated build, test, and deployment of compliance-sensitive financial data workflows while maintaining traceability, version control, and audit readiness aligned with regulatory standards. • Engineered real-time streaming pipelines using Apache Kafka, Azure Event Hubs, Apache NiFi, and Apache Flume, with monitoring dashboards in Grafana and Prometheus, delivering operational insights into transaction throughput, fraud alerts, payment failures, system performance, and compliance monitoring.