Rahul Shrimali

About Rahul Shrimali

Quality Assurance professional with 6.5+ years of IT experience and proven expertise in Data Warehousing, ETL, and Database Testing. Over 4.5 years of experience in directly Interacting with client QA/Test Teams. In-depth experience in multiple DW ETL projects using tools like SSIS, Informatica, Databricks, Hive, Unix Systems, and databases like Oracle, MySQL, Teradata, MongoDB, and Snowflake, etc. Good knowledge of MS SQL Server, Management Tools such as RALLY,HP ALM,JIRA, ADO. Project experience with Financial, Insurance, Banking, and Telecom domains, covering areas such as Insurance Compliance, Property and Health Insurance, Employee Group Benefit PI Insurance, and Banking Revenue & Service Reports. Client handling experience across multiple projects with Cisco, Zensar and MMC (US-based clients). Good experience in driving end-to-end Projects & QA/Testing processes across Agile (Scrum)+ Waterfall delivery methodology along with heading the Test Management Approach.• Handled numerous challenging responsibilities on Offshore team, provided QA Support, and served as a Backend Subject Matter Expert (SME). Experienced Scrum planning call with a keen ability to assess team efforts, estimations, and system complexities. Experienced in Agile methodology framework, leading Sprint Planning, Reviews, Retrospectives, and Daily Stand-ups to drive product delivery. Proven ability to collaborate in client-facing roles and cross-vendor teams.

Experience

1. Quality Assurance Specialist

   LTIMindtree

   Aug 2024 — Present · Indore, IN

   Understand insurance processes: policy management, claims, underwriting, premium calculation.Familiarity with insurance data types: customer, policy, claim, agent, product, and risk data.Extract data from various source systems: CRMs, policy admin systems, claim processing systems.Data may come from legacy systems, flat files, XMLs, JSON, relational databases, etc. Validate source-to-target data mapping using mapping documents.Ensure correct policy numbers, claim IDs, premiums, customer details are extracted.Ensure correct loading into data warehouse or reporting data marts.Validate dimensions (e.g, time, policy type) and fact tables (e.g, claims, transactions).Data quality check Duplicate checks (e.g, no double claim entries), Null checks (e.g, no missing policy start dates),Data type and length validation.Match total policies, claims, and premium amounts between source and target systems.Working on layer-to-layer data validation.Tools – Hive, SQL Developer, Data bricks (Midas), Azure Dev

Education

• University Institute of Technology, RGPV

  Bachelor of Engineering - BE

  2008 — 2012