ANUJA MANWARE

• End-to-End Pipeline Engineering: Architected and optimized distributed cloud ETL pipelines utilizing Python, SQL, and ADLS, reducing batch data latency by 75% while boosting ingestion throughput.
• Real-Time Stream Processing: Deployed an Apache Kafka pub/sub messaging infrastructure for enterprise-scale streaming, enabling low-latency downstream data consumer feeds and live operational reporting.
• Cloud Storage & Governance: Implemented dual-tier cloud storage architectures across Azure Data Lake Storage (ADLS) and Amazon S3, ensuring strict compliance with regulatory standards and enterprise data governance frameworks.
• Automated API Ingestion: Built multi-format parsing pipelines handling semi-structured files (CSV, XML, XLSX, DAT), integrating Windows Graph API to automate mailbox attachment retrieval and raw data staging.
• Data Cleansing & Quality: Created rigorous automated data validation, profiling, and sanitization routines to detect schema anomalies, guaranteeing 99.8% data integrity across analytical warehouses.
• Business Intelligence Delivery: Designed executive Tableau dashboards integrated directly with analytical SQL data layers, transforming complex multi-source metrics into clear strategic KPIs.
• Technical Standards: Standardized full-lifecycle data engineering documentation, workflow schematics, and runbooks, accelerating developer onboarding and auditing compliance.
• Technologies: Python, SQL, Apache Kafka, Azure ADLS, Amazon S3, Tableau, MySQL, Pandas, NumPy, Windows Graph API

• Partner Data Onboarding: Engineered scalable generic onboarding data pipelines for Tier-1 OEM partners (Humax, Airties, Nokia, Gemtek, Comscope), reducing overall ingestion turnaround time by 70%.
• Automated Pipeline Orchestration: Developed automated multi-stage ETL workflows executing parallel extraction, conversion to CSV, staging in ADLS, loading to relational SQL tables, and archival into Amazon S3.
• High-Volume Transaction Processing: Scaled data pipelines across 14 tables processing approximately 10 million transaction records per table per OEM without pipeline stalls.
• Automated Quality Alerts: Constructed an automated exception-handling framework integrated with SMTP alerting APIs to instantly flag format variances and data anomalies to external OEM partners.
• Production Deployment
• Tech Stack: Python, SQL, Azure ADLS, Amazon S3, SMTP / Email Notification APIs

• Statistical Computing Pipeline: Built high-performance parametric data calculation pipelines using Pandas and SQLAlchemy to ingest high-frequency shopfloor manufacturing telemetry.
• Statistical Quality Controls: Programmed statistical metrics and distribution hypothesis test suites including Process Capability Indices (Cp, Cpk), Upper/Lower Control Limits (UCL/LCL), Variance, Kolmogorov-Smirnov, Shapiro-Wilk, and Anderson-Darling tests.
• Production Analytics
• Tech Stack: Python, Pandas, NumPy, SciPy, SQLAlchemy, pyodbc, Requests

• Query & Procedure Tuning: Engineered high-efficiency T-SQL stored procedures, user-defined functions, triggers, and views, achieving a 50% reduction in query runtimes and 25% lower response latency.
• Dimensional Architecture: Built star-schema warehouse architectures with Fact tables, Dimension tables, and Slowly Changing Dimensions (SCD) via SSIS ETL, increasing analytical query performance by 40%.
• Index Strategy: Conducted execution plan analysis, index defragmentation, and query restructuring, yielding a 15% increase in overall database transactional throughput.
• Database Optimization
• Tech Stack: MS SQL Server, T-SQL, SSIS ETL, Dimensional Modeling, Oracle, Flat Files

GPA: 85%

GPA: 8.49 / 10