Sriram K S
Analyst
Bangalore
Summary
With 3.5+ years of experience in data analysis and business partnering, I bring expertise in SQL, Tableau, and G-Sheets to deliver insights and drive decisions. Skilled in automation and teamwork, I leverage attention to detail and strong listening skills to solve problems effectively.
Overview
8
8
years of professional experience
6
6
years of post-secondary education
1
1
Certificate
Work History
Analyst
Salesforce
01.2023 - Current
- With an objective to have live data, graphs for KPI for a multi-year time frame with data volume over 1 lakh rows across the GTM structure. This platform would allow faster processing of quota changes and submissions during the maintenance phase, provide ready KPI metrics to the management with no manual errors. To materialize this, I developed the Quota Maintenance tracker using Tableau and Tableau Prep, reducing a 6-hour per week manual ETL process to under 30 minutes per week. Enabled hourly data refreshes, improving efficiency by over 80% and ensuring superior data accuracy.
- With the aim of automating Ramp and Seasonality combo creation across the GTM structure. This automation would reduce the time taken to manually create and load the Ramp and Seasonality combos during G4G. I designed a Google sheet project to automate the creation of Ramp and Seasonality combos to streamline combo loading, reducing time and increasing process efficiency by over 50%, enabling faster and more reliable combo generation
- Our objective was to analyze the average quota across operating units and market segments while examining year-over-year (YoY) growth trends. This analysis enabled us to determine if quota allocations required adjustments to ensure an optimal distribution of capacity. To support this effort, I created a model that integrated historical data with the current go-to-market structure, delivering key insights and actionable recommendations to improve the quota allocation process
- I designed a model to track executive and senior leadership quotas across all operating units, directly impacting Salesforce leaders and drivers. This initiative enhanced efficiency by 7% and significantly reduced manual errors in downstream systems, ensuring improved accuracy and reliability.
- With an intention of keeping in sight the quota reliefs FLM receive when their AE's are on prolonged leave, I drafted and implemented a streamlined process to track quota relief for FLMs when their AEs were on leave, significantly reducing the need for manual calculations unlike previously used methods. This improvement increased efficiency by 60%, enabling faster quota renewals and compensation delivery to FLMs during G4G and the final quarter of Salesforce's fiscal year
- To analyze the average New Logo quota across operating units and market segments while examining year-over-year (YoY) growth trends. This analysis enabled us to determine if New Logo quota allocations required adjustments to ensure an optimal distribution of capacity. To address this I came up with a Tableau dashboard to monitor the average number of new companies Account Executives introduced to the Salesforce ecosystem. Migrated the process from Google Sheets to Tableau, enabling live data updates, improving efficiency by 30%, and supporting leadership in setting new logo targets for the G4G initiative in the following year
- Enhanced the ETL process critical to the maintenance phase of Salesforce's fiscal year, improving efficiency by 11%. Later, automated the process using Tableau, significantly boosting efficiency and providing stakeholders with live data, while minimizing manual errors
- With the intent of having streamlined processes for tracking quota relief for AEs on extended leave, I composed a file implementing the same and also improving efficiency by approximately 10%. This led to faster delivery of quota adjustments and compensation during the G4G period and the last quarter of Salesforce's fiscal year
Data Analyst
Fincare Small Finance Bank
01.2020 - 04.2020
- Loan Risk prediction using excel and SQL DB, increased efficiency by at least 15%
- Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models with an accuracy of 73 %
- Collaborating with the business team for the implementation of the aforementioned pronouncement thus reducing the fraud by 11 %
- Connected SQL Server to Python using the Python ODBC library.
Software Engineer
Societe Generale
08.2016 - 01.2018
- Analyzing and working on at least 10 front end feature enhancements every quarter
- Resolved at least 15 front-end and back-end data procurement bugs in the System Risk project using Java and SQL
- Contributed to the Anti-Money Laundering (AML) project
Education
PGD - Data Science
02.2019 - 01.2020
B.E - Information Science and Engineering
08.2011 - 06.2016
Skills
SQL - RDBMS
Joins
Sub-queries
Analytical Functions
Group Functions
MS Excel
Google Sheets
Tableau
Tableau Prep
Snowflake
ETL
Certification
PGP - DSBA from Great Learning, 07/01/22, 06/30/23
Timeline
Analyst - Salesforce
01.2023 - Current
Data Analyst - Fincare Small Finance Bank
01.2020 - 04.2020
Manipal Academy of Higher Education - PGD, Data Science
02.2019 - 01.2020
Software Engineer - Societe Generale
08.2016 - 01.2018
The National Institute of Engineering - B.E, Information Science and Engineering
08.2011 - 06.2016