Tousib Sarker.

Complex problems.
Clear products.
Measurable impact.

I’m Tousib, a Product Lead and Technical Business Analyst. I turn complex business needs into useful products, reliable data, and simpler ways of working.

Explore my work Download résumé

New York City  ·  5+ years of experience  ·  U.S. citizen

Experience across
Moody’s RatingsMaterial BankVertivBlackstone

01 / Selected work

The problem. The work. The outcome.

Three examples of how I connect business requirements, product delivery, and reliable data.

01 / Moody’s Ratings

From legacy tracker to integrated review workflows

Modernizing the Credit Fitness Tracker with Microsoft Power Platform, connecting review workflows and management reporting.

Product deliveryWorkflow designPower Platform
End-to-end ownershipRequirements, backlog, UAT, and release readiness
Explore the case study

The business problem

The Credit Fitness Tracker was a legacy application supporting review activities. The modernization brings review workflows and management reporting into an integrated Power Platform application.

My contribution

I own requirements, solution design, backlog prioritization, UAT, release readiness, and stakeholder adoption. My hands-on work includes Power Automate workflows and Power BI reporting.

Approach

  1. Translate Ratings business requirements into product features, workflow rules, reporting logic, and technical specifications.
  2. Build automated review calculations, aggregate participant and rating-group data, and synchronize reporting fields.
  3. Design reporting for review volume, participant counts, rating groups, year-based filtering, and weighted measures.
  4. Coordinate testing, defect resolution, and release readiness.
Scope at a glance
  1. Review inputs
  2. Automated calculations
  3. Reporting data
  4. Management views

This simplified workflow illustrates the scope described above. It is not an employer screenshot or a production architecture diagram.

Outcomes

The work connects review activities, automated calculations, and management reporting. It is presented here as an ongoing modernization initiative; no quantified adoption or efficiency result is claimed.

Lessons learned

Moody’s Ratings: Define the rules before automating the workflow

A workflow is only as reliable as the business rules behind it. Clear definitions for calculations, ownership, and exceptions make requirements easier to build, test, and explain. My takeaway: treat reporting and UAT as part of product discovery, so the team agrees on what “correct” means before release.

02 / Material Bank

Scaling product-data quality through automation

Automating enterprise data-quality validation and strengthening product-data synchronization to reduce manual remediation.

Data qualityAirflowSQL
40% less manual remediationThrough automated data-quality validations
Explore the case study

The business problem

Product-data operations involved manual remediation and metadata discrepancies. The work focused on automating validation and improving synchronization between EnterWorks and Magento.

My contribution

I built Airflow DAGs for enterprise data-quality validation. In related synchronization work, I redesigned the EnterWorks-to-Magento integration and implemented SQL validation rules.

Approach

  1. Build automated data-quality validations in Airflow.
  2. Introduce SQL validation rules within the synchronization improvement effort.
  3. Redesign EnterWorks-to-Magento synchronization to reduce metadata discrepancies.
Scope at a glance
  1. Product data
  2. Automated validation
  3. Quality findings
  4. Remediation

This conceptual process summarizes the validation work. It uses no client records or internal implementation details and is not an actual employer deliverable.

Outcomes

Automated validations reduced manual remediation by 40%. The related synchronization and SQL-validation work reduced metadata discrepancies by 20%. These are distinct results from related initiatives.

Lessons learned

Material Bank: Turn recurring fixes into repeatable controls

Resolving a data issue once helps today; identifying the pattern can prevent the same work tomorrow. My takeaway: use recurring quality problems to guide automated validation, and make the findings actionable for the people responsible for resolving them. Measure success through reduced manual effort and fewer discrepancies, not simply the number of checks running.

03 / Vertiv

Migrating five million product records

Delivering an Oracle-to-PIM migration and designing master data models for a more reliable product-data foundation.

Data migrationPIM / MDMBusiness analysis
5M+ records · 4 monthsOracle-to-PIM migration delivered
Explore the case study

The business problem

The initiative moved a large volume of product records from Oracle into an enterprise PIM platform, alongside work to improve the accuracy of master product data.

My contribution

I delivered the migration of more than five million product records within four months and designed scalable master data management models.

Approach

  1. Deliver the Oracle-to-enterprise-PIM product-record migration.
  2. Design scalable master data management models to improve product-data accuracy.
  3. Support sales and operations through SQL, Python, and Power BI analytics in the broader role.
Scope at a glance
  1. Oracle product records
  2. Product-data migration
  3. Enterprise PIM
  4. Master data models

This simplified system flow shows the scope of the migration and data-modeling work. It is a conceptual illustration, not the original migration plan or production architecture.

Outcomes

More than five million product records were migrated within four months. The master data management models improved data accuracy by 25%.

Lessons learned

Vertiv: A migration is also a chance to improve the foundation

Moving records is only part of a successful migration. The destination needs consistent definitions and data models that remain useful after the transfer. My takeaway: define data-quality expectations alongside migration goals, so speed and volume do not become the only measures of success.

02 / Experience

Business context. Technical depth.

Moody’s Ratings

Sep 2025 — Present

Product Lead | Technology Business Analyst

  • Lead enterprise application delivery supporting Ratings and methodology governance, from requirements to stakeholder adoption.
  • Translate complex business requirements into product features, workflow rules, reporting logic, and technical specifications.
  • Own UAT and defect resolution across methodology governance applications.

Material Bank

Jul 2022 — Aug 2025

Senior Technical Business Analyst | Business Analytics

  • Led three enterprise technology initiatives valued at $5M–$10M+, coordinating delivery across 20+ stakeholders.
  • Implemented AI-assisted classification workflows that improved catalog categorization and search accuracy by 25%.
  • Led product-data quality initiatives across 600+ client brands and drove Agile delivery to 98% on-time feature completion.

Vertiv

Jun 2021 — Jul 2022

Business Analyst | Sales & Operations Data Analytics

  • Led two analysts within a global analytics organization, delivering reporting across 5,000+ enterprise clients.
  • Built Python, Power BI, and SQL dashboards for account growth and revenue performance.
  • Created sales-attribution reporting that contributed to $100K+ in commissions.

Earlier experience

Blackstone · Business Analyst, Operations · Launchpad Internship, 2020
TVP.NYC · Business Analyst, Financial Analytics · Internship, 2017

03 / Expertise

Connecting strategy and execution.

Product & delivery

Discovery and requirements
Backlog prioritization · Agile / Scrum
Process design · Stakeholder alignment
UAT · Release management · Adoption

Analytics & insight

SQL · Python · Power BI
Tableau · Looker · Alteryx
Executive dashboards · KPI reporting
Excel · Forecasting · Data validation

Automation & platforms

Power Apps · Power Automate · Airflow
ETL · dbt · PIM / MDM
AWS · Azure · Snowflake
SharePoint · EnterWorks · Oracle Cloud ERP

04 / Education

University at Buffalo

B.S. in Business Administration &
Management Information Systems

Vice President, Delta Sigma Pi

English · Native
Bengali · Fluent
Spanish · Intermediate

05 / Certifications

Learning that supports the work.

Credentials across product delivery, cloud and AI, process improvement, finance, and technology.

Let’s connect

Have a business problem to solve?

I’m interested in opportunities in product management, product ownership, product and business analysis, and consulting.

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