Mar 2023 – Present · 3 yrs 3 mos

• 📍Driving Elastic RTE readiness for 07.M and 10.M by testing and certifying 69 connectors and resolving critical compatibility issues across pods.
• 📍Owning CCI performance engineering, including design of the JMeter-based Multi-Org Mapgen Load Framework. Automated org/MCT flows, validated SAAS API scalability, demonstrated 400 Mapgens in 60 seconds, and established rate-limits and stability criteria post K8s onboarding, and collaborated with DevOps to optimize Grafana dashboards.
• 📍Led deep Performance analysis of the RAG pipeline (Parser → Embedding → Chunking → Pinecone Vector DB), highlighted issues that delivered ~70× performance improvement for large files and ~22× for multi-file workloads.
• 📍Developed Version 2 of the Grafana + Glances + InfluxDB NF-testing utility to improve installation reliability and enable testing teams to capture resource monitoring across cloud and on-prem VMs for NF Testing.
• 📍Led performance and functional validation for First Class ELT across Databricks, Fabric, GBQ and Snowflake; identified 16+ cross-ecosystem issues, drove fixes with engineering teams, and ensured complete regression coverage with zero customer impact.
• 📍Drove INFACore performance strategy through Mapping and Jobs API stress/load tests, Arrow connector benchmarking, Mapplet throughput analysis and identification of OOM, buffer and execution overhead issues. Derived optimal Mapplet counts, evaluated Serial/Parallel/Hybrid arrangements, set API rate-limits in NFR.
• 📍Led INFAConnect performance analysis across Search, Manage, Publish, Download and Post-to-Prod APIs. Identified delays in SAST scans, polling issues in SCA scans and high latency in Search Connector. Improved Tomcat logging, derived API rate-limits, monitored K8s CPU/memory.
• 📍Validated ~23× gains in Oracle CDC → Snowflake ingestion through analysis of a decoupled Put/Copy design and benchmarking of CDC property combinations.

Apr 2020 – Mar 2023 · 2 yrs 11 mos

• 📍Led performance engineering activities across NoSQL space including MongoDB, CosmosDB and DynamoDB Connectors; profiled throughput, identified bottlenecks, validated fixes for wide-row and high-volume datasets, and improved connector stability across releases.
• Performed extensive benchmarking for SFDC Bulk API V2 vs V1, highlighted 2× slowness in V2, documented and worked with salesforce to dig deeper. Executed large-scale SFMC performance tests and validated ~40× improvement through batch-size and heap tuning.
• 📍Evaluated and validated performance of the OCI Object Store connector for MVP readiness by benchmarking read/write patterns, identifying regressions, and recommending configuration-level optimisations.
• 📍Proposed and validated the Exact Path Implementation for Synapse SQL, confirming 3.5× faster Mapgen performance and improved stability for deeply nested datasets. Also provided POC guidance for JDBC-based Synapse reads to reduce dependency on cloud staging.
• 📍Identified and validated a >50% latency regression in Test Connection flows via Python SDK (JLI), provided detailed RCA, and drove fixes with owning engineering teams.
• 📍Provided performance sizing and infrastructure recommendations for Data Loader GA, delivering detailed guidelines for deploying CCI, SaaS and Cloud Agents across Azure, AWS and GCP using node-level telemetry and Grafana dashboard.
• 📍Led performance analysis for hierarchical and complex datatypes across connectors, ensuring consistent handling of nested and semi-structured data formats.
• 📍Conducted multi-cloud competitor benchmarking (ADF vs CDI/CDI-E vs standalone SQL/Scala) across Azure DW, ADLS Gen2, Blobv3, Synapse SQL, Snowflake and GCP ecosystems; delivered insights that influenced connector tuning, roadmap priorities and customer guidance.
• 📍Collaborated with PM and engineering teams to support customer escalations and performance POCs, ensuring data-driven decisions and improving reliability of critical integration workloads.

Feb 2018 – Mar 2020 · 2 yrs 1 mo

• 📍Performed performance engineering across widely used Azure connectors including ADLS Gen2, Blob and Synapse SQL. Benchmarked these against Microsoft ADF and standalone Scala notebooks to establish tuning formulas for concurrency, file sizing, partitioning and JVM parameters.
• 📍Built the AWS Account Stats automation (detailed in Projects) to track multi-account usage, visualize cost patterns and notify stakeholders — improving cloud cost governance and contributing significant annual savings.
• 📍Identified inefficiencies in connector compression paths and recommended enabling Parallel GZIP for faster data movement, validating 10–15× faster compression across Azure connectors and driving broader adoption across S3, GCS and related ecosystems.
• 📍Executed scaling, sizing, profiling, sampling, concurrency and stress tests to define connector behaviour under different workload patterns. Performed cross-connector validation for wide-row and nested datasets to ensure consistent throughput and memory performance.
• 📍Contributed to PolyBase pushdown analysis for ADLS Gen2 → Azure DW, helping validate significant performance gains and guiding tuning strategies for large dataset workloads.
• 📍Built and validated the AWS Usage Analytics Framework using AWS SDKs (EC2, EMR, RDS, Redshift) to help engineering and leadership teams understand usage and cost patterns; introduced automated dashboards summarising daily and weekly cloud activity.
• 📍Automated regression and baseline performance validation for Azure DW, ADLS Gen2 and Blob connectors, ensuring predictable performance across releases and cloud configurations. Rewrote and validated tuning guides for ADLS Gen2 and SQL DW to improve clarity and adoption by PM, Support, and customer-facing teams.
• 📍Conducted competitor analysis to ensure Informatica connectors remained on par or ahead of third-party alternatives, supporting product positioning and engineering prioritizations.