-Worked on functional design and guided team for adding various enhancements in Flat File reader/writer like processing Spark partitioning data, multi-file read using wildcards, and recursive read support in case of nested directory structures.-Designed and helped in implementing Connection Override facility for modifying any connection attribute (For example - oracle username/password) by passing values in a parameter file during Informatica mapping execution.-Designed and implemented Heartbeat mechanism for getting runtime status of data processing jobs and gracefully handling failures due to issues like resource congestion, network problems, thread deadlocks etc.-Explored Kubernetes as a Yarn alternative for managing resources during a data processing job on Informatica Big Data Edition. Also researched on ways to automate creation of temporary multi-node Kubernetes clusters during mapping runtime.

-Worked on PC Reuse project consisting of converting mapping (data processing job) metadata from Informatica PC (PowerCenter) to Informatica BDE (Big Data Edition), helping customers easily move their existing mapping logic from a single server to a Hadoop environment.-Improved sorter performance by increasing the maximum record size that can fit in-memory during sorting operations, which also improved the performance of transformation algorithms relying on sorted data like aggregator, rank, and joiner.-Fixed multiple  functional/performance bugs in customer production environments and major/minor releases.-Created Admin UI for REST interface support in Informatica BDE (Rest Operations Hub).-Added support for alter table queries while writing data into a Hive table using Hive QL or Spark QL.-Designed and implemented functionality to compare and report differences between two mapping metadata objects in a tree-like parse-able format as part of CI/CD API support.

-Worked on several customer production bugs in Informatica PC (PowerCenter), primarily issues in data transformation algorithms, intermediate memory buffers, and data partitioning logic.-Added support for new Hadoop clusters like Microsoft HDInsights 3.4 and IBM BigInsights 4.1 in Informatica BDE (Big Data Edition).-Fixed multiple release/production issues in Hive and Spark engines for various supported Hadoop distributions like Cloudera, Hortonworks etc.-Contributed towards upgrading Hive engine support to Thrift server in Informatica BDE.

-Runtime performance optimization of Lookup and Joiner Transformations in the Informatica Native Data Processing Engine by compressing incoming data to fit into in-memory cache, thereby reducing disk I/O.