Mounika Kancharla

Technologies: AWS Services(Glue, EMR, SQS, S3, Athena), Spark, Terraform, Python

• Led a team of five to migrate the Security Classification Mechanism from a legacy platform to AWS, improving scalability and modernization across the application.
• Executed the NADC migration end-to-end, coordinating seamlessly with multiple cross-functional teams to ensure a smooth and uninterrupted transition.
• Directed multi-application resiliency (SRE) and network isolation initiatives that proactively reduced risk of application outages and minimized user-impacting downtime.
• Fostered strong collaboration through consistent communication with internal and external stakeholders, enabling efficient delivery of complex engineering tasks.
• Implemented a structured knowledge-sharing framework that improved team efficiency and reduced recurring issue resolution time.
• Partnered with platform engineering teams to enhance system stability and optimize performance for large-scale data processing workloads.

Technologies: AWS Services, Airflow, Linux OS, HDFS, hive, Python

• Led the redesign and migration of source system integrations to eliminate uncertified connections, transitioning to standardized NDM-based data exchange for improved security and compliance.
• Directed the architecture and migration of applications from physical servers to Virtual Server Infrastructure (VSI), enhancing scalability, reliability, and resource utilization.
• Designed and implemented a centralized authentication cache layer to optimize interactions with Hadoop (HDFS), improving performance and reducing repeated authentication overhead.
• Standardized all applications on the firm’s encryption protocols, reducing reliance on external services and lowering associated operational costs.
• Architected secure upstream connectivity aligned with audit and security requirements, eliminating unsecured interfaces and mitigating compliance vulnerabilities.

Technologies: Scala, Hadoop FS, Spark, Kafka, Zookeeper, Hive, Rest Api

• Designed and developed a passwordless authentication framework for Spark-based data ingestion into Cassandra, enabling secure, seamless user authorization and improving pipeline reliability.
• Architected a standalone ingestion pipeline leveraging Kerberos authentication to transform and load large-scale datasets into Cassandra.
• Designed and implemented a lightweight Scala-based microservice to replace dependency on legacy scheduling tools, eliminating over 400 Autosys jobs and significantly reducing operational overhead in production.

Technologies: Hive, SQL, HDFS, Autosys

• Designed and supported an event-driven ingestion pipeline using Spark to load and process incoming data files into Hadoop-based storage systems.
• Collaborated with the Data Modeling team to align ingestion logic and transformation rules with defined data models and project requirements.
• Implemented secure file transfer mechanisms between source systems and application servers, ensuring compliant and reliable data movement.

Technologies: SQL, Informatica Power Center, MQ- Series for Messaging

• Integrated MQ topics with Informatica PowerCenter to enable near–real-time ingestion and processing of client transaction messages.
• Developed end-to-end ETL mappings using CDC-1 methodology to extract, transform, and load streaming data into dual database targets in parallel.
• Improved data availability for business advisors by ensuring processed data was delivered before end-of-day decision-making cycles.

Technologies: SQL, Informatica Power Center, Control-M for Scheduling

Technologies: SQL, Informatica Power Center