Aleksandra Pakhomova

Managed complex BI projects for Nike, McKesson, H&M, Burberry, Zalando from inception to completion, consistently delivering high-quality results within budgetary constraints and strict timelines.

• Starting from Solution Design stage, developed a universal Framework for evaluation success for 5 types of Data Products (89 Data Products in Data Analytics Product Management Department portfolio)
• Gaining exposure to the product management process by working with multidisciplinary teams, including Heads of Departments, Product Managers, Technical Engineers and Business Stakeholders
• Generate reports and presentations on results findings during development stage, monitoring and analyzing Stakeholders feedback
• Established methods to collect internal customer feedback, developed mock data and templates for visualization
• Developed high-quality documentation to capture requirements and key processes; created guidelines to support training efforts, helping employees fully understand new procedures.

• Successfully led the lifecycle of a new product from Discovery to Production, overseeing requirements gathering, design, development, testing, and deployment to ensure timely delivery
• Simultaneously actively participated in the development of 3 innovative data-related applications, contributing to engineering activities and ensuring alignment with project objectives and stakeholder expectations
• Communicated and collaborated with Product Owners to gather and clarify requirements, translating business needs into actionable tasks for development team, and ensuring successful project execution
• Managed Product Backlog efficiently by creating Epics and User Stories, prioritizing tasks to ensure streamlined delivery of developed solutions and maintaining project timelines
• Orchestrated collaboration among multidisciplinary teams, including ML specialists, Frontend and Backend Developers, Data Engineers, and QA Engineers, to drive successful project outcomes

The main goal of 10-week period was a migration of the current ML pipeline in production to Google's Vertex AI platform. Due to an issue with gaining access to source code and data, the new proposal for the project was to create a ML pipeline in Vertex AI using public H&M's data set in Kaggle to demonstrate Vertex AI and its ability to orchestrate. As Business Analyst, I:

• Conducted Discovery phase
• Participated in Requirements clarification and prioritization sessions
• Participated in revision and prioritization of new deliverables due to an access issue
• Supported Build phase, KT & DEMO sessions according to the new list of deliverables
• Created project documentation and reports, provided regular project status updates to Stakeholders

• As part of EPAM Team, I supported Data Migration of about 400 Contractual Data Feeds to Snowflake
• Developed comprehensive requirements for approximately 200 (of 400) Contractual Data Feeds, ensuring alignment with company principles and standards
• Conducted thorough analysis of data in both source and target databases, examining the structure, content, and relationships to ensure reconciliation
• Investigated source SQL scripts, documentation, and data samples from source reports to establish a clear understanding of the data elements and their corresponding mapping to the target database, meeting the requirements of third-party Vendor's systems
• Collaborated in requirements clarification sessions to gather and understand the specific needs and expectations related to Contractual Data Feeds and underlying company data structure
• Actively participated in Quality Gates creation (facilitated brainstorm session, managed the discussion and shared ideas about Quality Metrics, developed documentation)
• Generated project documentation using Confluence, documenting mappings, refactoring process and guidelines for future reference and knowledge sharing
• Prepared to UAT: agreed the list of participants; visualized UAT process step by step using diagrams, described it in the project documentation and partnered with Stakeholders to ensuring alignment with company goals, described possible risks and risk management measures
• Facilitated knowledge transfer and conducted DEMO sessions to effectively communicate solutions to stakeholders and team members
• Guided 4 new Data Analysts through project onboarding, providing mentorship to ensure a seamless integration and swift contribution to team goals

• As part of EPAM Team, I participated in Data Migration for Nike China and conducted comprehensive analysis of 5 source tables and data availability; developed thorough data profiling, identifying key data attributes, structure, and quality;
• Created robust S2T mapping, establishing clear connections between source data and target repositories for accurate data consolidation and management;
• Conducted data validation and performed in-depth investigations of discrepancies between legacy databases and target requirements, ensuring data accuracy and consistency;
• Designed and executed comprehensive test cases to validate the functionality and performance of Airflow pipelines, including data quality checks and data loading processes into target objects;
• Contributed to the creation of data models, ensuring data structures align with business processes and enable effective data governance;
• Validated the metadata log, ensuring accuracy, completeness, and consistency of metadata records within the system;
• Developed test scenarios and test cases specifically tailored for UAT phase, ensuring thorough validation of solutions against user requirements and expectations;
• Active participation and support throughout the UAT process, collaborating with users to address feedback, resolve issues, and validate the effectiveness of solutions;
• Participated in KT and DEMO sessions