Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Engineering | Semantic layer | Conversational Analytics | Snowflake | dbt | Agentic AI | Cloud Data Migration | Azure | GCP | AWS | Snowflake Cortex Code | Data Lake | Batch & streaming pipelines | Data for AI
Data Engineering leader with 14+ years of experience building scalable cloud data platforms, modern semantic layers, and AI-ready analytics ecosystems. I specialize in delivering high-impact data engineering solutions that improve data accessibility, reduce processing time, and enable faster business decision-making at scale. My expertise spans Snowflake, dbt, Python, Azure Data Factory, Azure Synapse, BigQuery, Power BI, Tableau, Looker, and modern batch & streaming architectures. Over the years, I have led multiple cloud migration and modernization programs, architecting resilient data platforms supporting enterprise analytics, self-service BI, conversational analytics, and AI-driven data experiences. I am particularly passionate about enabling “Data for AI” initiatives through semantic modeling, governed data products, scalable ELT frameworks, and emerging capabilities such as Agentic AI, GenAI-powered analytics, and Snowflake Cortex. Leadership & Delivery Highlights ➤ Led architecture and delivery of enterprise cloud migration initiatives, including BigQuery to Snowflake modernization programs, from requirement gathering through production rollout ➤ Defined migration roadmaps for legacy on-prem ETL platforms to cloud-native architectures using Snowflake, dbt, and ADF ➤ Built scalable and reusable ELT frameworks that improved pipeline reliability, reduced operational overhead, and accelerated onboarding of new data sources ➤ Enabled faster analytics adoption by developing governed semantic models and self-service reporting solutions across multiple business domains ➤ Developed Python-based automation frameworks for monitoring, reconciliation, metadata-driven processing, and operational optimization ➤ Explored and implemented AI-driven analytics use cases including conversational querying, semantic search, and LLM-assisted data discovery ➤ Partn
Amity University Lucknow
Bachelor of Technology (B.Tech.), Computer Science
January 1, 2007 – January 1, 2011
B.R Inter College
12th, Science
January 1, 2005 – January 1, 2006
B.R Inter College
10th, Science
January 1, 2003 – January 1, 2004
Everpure
Data Engineering Lead
October 1, 2023 – Present
Bengaluru, Karnataka, India · Hybrid
Deliveroo
Analytics Engineer
January 1, 2023 – October 1, 2023
Bengaluru, Karnataka, India · Remote
Citrix
Senior Data Engineer || Enterprise Data Platform
April 1, 2020 – January 1, 2023
On-site
Citrix
Senior Data Engineer I (Sales & Go-to-Market Analytics)
December 1, 2018 – March 1, 2020
On-site
CGI
Senior Data Engineer - Cloud
October 1, 2016 – November 1, 2018
Shell India (Bengaluru Area, India) · On-site
Cognizant
Business Intelligence Developer
December 1, 2014 – September 1, 2016
Bangalore · On-site
Accenture
Software Engineering Analyst
September 1, 2012 – November 1, 2014
On-site
Accenture
Software Engineering Associate
June 1, 2011 – August 1, 2012
On-site
Certified SAFe® 5 Practitioner
SAFe by Scaled Agile, Inc.
June 23, 2026 – Present
Azure Data Engineer Associate (DP-200 & DP-201)
Microsoft
June 23, 2026 – Present
Querying Microsoft SQL Server (70-461)
Microsoft
June 23, 2026 – Present
SnapLogic Workshop 101
SnapLogic
June 23, 2026 – Present
Data Analyst Associate PowerBI (DA-100)
Microsoft
June 23, 2026 – Present
Azure Data Fundamentals (DP-900)
Microsoft
June 23, 2026 – Present
Azure Fundamentals (AZ-900)
Microsoft
June 23, 2026 – Present
Cultural Fit Analysis
The candidate has worked in various large organizations and tech companies, indicating adaptability to different corporate cultures. The progression through roles from Software Engineering Analyst to Data Engineering Lead suggests a growth mindset and ambition. The breadth of skills and tools used across different projects and companies indicates a willingness to learn and integrate new technologies, which is positive for cultural fit in dynamic environments. However, without specific project details or behavioral assessment, a deeper cultural fit analysis is limited.
Soft Skills & Operational Fit
The candidate's extensive experience across multiple companies (Accenture, Cognizant, CGI, Citrix, Deliveroo, Everpure) suggests adaptability and exposure to diverse operational environments. Roles like 'Data Engineering Lead' and 'Senior Data Engineer' imply leadership, problem-solving, and collaboration skills. Agile methodology experience further supports operational fit.