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Data Engineer with 5+ years in Azure Data Engineering, ETL, and Automation Testing.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
QA & Azure Data Engineering Professional with 5+ years of experience combining strong Quality Assurance background with hands-on Azure Data Engineering expertise. Skilled in Azure Databricks, Azure Synapse, ADF, ETL, SQL, automation testing, and cloud-based data pipelines. Seeking challenging opportunities to leverage end-to-end data engineering and testing capabilities.
Trident Academy of Technology
Master of Computer Applications (MCA)
August 1, 2018 – June 30, 2020
MBS Global (Xebia)
Sr QA Data Engineer
March 1, 2026 – Present
India
Xcaliber Infotech
Azure Data Engineer
October 1, 2023 – September 1, 2025
India
Cognizant Technology Solutions
Quality Analyst
April 1, 2022 – April 1, 2023
India
Apmosys Technology Pvt. Ltd.
Automation Test Engineer
December 1, 2019 – April 1, 2022
India
Sales Analytics Platform (Azure Data Engineering)
June 28, 2026 – Present
Migrated on‑prem SQL workloads to Azure using ADF copy activities. Created unified data model for sales KPIs using Synapse SQL Pools. Used Databricks for cleansing, deduplication, and feature engineering. Integrated Power BI with Synapse datasets for business dashboards.
Customer 360 Data Hub (Azure Cloud)
June 28, 2026 – Present
Ingested data from CRM, ERP, and IoT devices using ADF pipelines. Built customer 360 datasets using Delta tables in Databricks. Applied data quality checks, audit logs, and lineage tracking. Created Gold layer aggregations for customer segmentation and churn prediction.
Azure Data Lake Modernization (Azure Data Engineering)
October 1, 2023 – September 1, 2025
Built end-to-end scalable data pipelines using Azure Data Factory and Azure Databricks. Implemented Medallion Architecture (Bronze → Silver → Gold) using Delta Lake. Performed transformation logic using PySpark notebooks in Databricks. Used Azure Synapse for analytics, reporting models, and SQL-based transformations. Optimized pipeline performance using cluster tuning and partition strategies.
AT&T Phoenix Group (QA)
April 1, 2022 – April 1, 2023
Performed UI, functional, regression and ETL testing. Created test cases, executed test cycles, logged defects in JIRA. Validated production data pre‑ and post‑deployment.
Cultural Fit Analysis
The candidate's project experience spans various data engineering scenarios (data lake modernization, sales analytics, customer 360), indicating adaptability and a broad understanding of data applications. The transition from QA to Data Engineering shows a proactive approach to skill development. The roles and projects align well with a data-driven culture, suggesting a good fit for organizations focused on leveraging cloud data platforms. The breadth of technical skills (Azure stack, PySpark, SQL, CI/CD) also points to a candidate who can integrate into diverse technical teams.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex data engineering tasks, suggesting problem-solving and analytical skills. The mention of CI/CD and Agile delivery implies an understanding of modern development methodologies and collaborative work environments. However, without specific psychometric or communication test results, a deeper assessment of soft skills like teamwork, stress handling, or communication clarity is not possible.