Prince Goyal

Data professional with 3+ years' experience in Data Engineering and Analytics. Proficient in Python, SQL, and PySpark, with expertise in Data Mesh Architecture. Strong leadership skills in team management and stakeholder engagement. Recognized for achievements in centralizing data, automating processes, and enhancing organizational efficiency.

• Microsoft Certified: Azure AI Fundamentals, I782-1756
• Microsoft Certified: Azure Fundamentals, I786-6555

• Accenture Q2 FY24 above and beyond Award
• Accenture H2 FY23 Start Award
• Client Value Creation award
• Quarterly and Annual best team award
• Top 10 in Palantir Hackathon Accenture Worldwide as Team Lead.
• Spot Award for collaborating for success, agility & learning in the organization.

Financial 360 Navigator - Dec 2022 to Present

• Subject Matter Expert for one of the 10 data products to build a Finance Summary dashboard to be consumed by Accenture’s Top 50 Executives, that ensures accuracy and trustworthiness, as endorsed by the CEO. Eliminated the need for Excel sheets by providing a comprehensive dashboard for finance review, streamlining processes and saving time.
• Led the effort to centralize finance data using the Data Mesh framework, enabled the transformation of data into valu- able insights for informed business decisions through Big Query.
• Created Python scripts for Azure DevOps Pipeline, automating the testing of PowerBl data with the capacity to handle over 400 distinct tests simultaneously.
• Oversaw a team of 3 data engineers, ensuring the excellence of their code through meticulous reviews.
• Facilitated discussions with the product owner to identify data needs and provide support for improved data decision-making and data logic.

DQ Engine (Accelerator) - May 2022 to Dec 2023

• Led the development of the backend prototype for the Data Quality (DQ) Engine Accelerator, which involved the evaluation of data quality and its remediation, thereby enhancing GenAl functionality to accelerate the firm’s business.

Single Customer Record - July 2021 to Dec 2022

• Developed health status visualizations using Elasticsearch (ELK) to ensure data completeness, timeliness and quality of customer enterprise data using automated python scripts to optimize data communication for rapid response for more than 15 matrices.
• Established seamless data communication using a caching layer (Redis) to achieve less than 500ms latency during 14,000 concurrent requests.
• Implemented log tracing and email alerts for proactive anomaly reporting, and automated scripts for generating lira tickets for prompt resolution and reduced the resolution time from 2 days to 1 hour.