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Data Engineer with 6+ years in Azure Data Engineering & Big Data Analytics.
Azure Data Engineer with 6 years expertise in Azure Data Factory (ADF), Azure Databricks, PySpark, Azure Data Lake (Gen1/Gen2), and Azure SQL Database. Experienced in ETL/ELT pipeline development, batch and real-time data processing, dimensional data modeling (Star & Snowflake Schema), and cloud-based data solutions. Proficient in Azure services including Logic Apps, Key Vault, and Azure Monitor. Strong focus on data quality, automation, performance tuning, and cost optimization. Microsoft Certified Azure Data Engineer Associate.
SRKR engineering college
B.Tech
N/A – June 30, 2020
LYROS TECHNOLOGIES
Swiss-Re Insurance Data Modernization Platform
June 1, 2023 – Present
Hyderābād, Telangana, India
ACCION LABS
Renaissance Consumer Data Platform (Coca-Cola)
April 1, 2022 – March 1, 2023
Hyderābād, Telangana, India
TECHROID SOLUTIONS
DIGITAL SALES
April 1, 2020 – March 1, 2022
Hyderābād, Telangana, India
Microsoft Certified: Azure Data Engineer Associate
Microsoft
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience across multiple companies (LYROS TECHNOLOGIES, ACCION LABS, TECHROID SOLUTIONS) and diverse client projects (Swiss Re, Coca-Cola) indicates adaptability and a broad exposure to different organizational cultures and business requirements. Their focus on mentorship, collaboration, and improving team productivity aligns well with a culture that values growth, teamwork, and continuous improvement. The breadth of their technical skills and project diversity suggests a strong cultural fit for dynamic and innovative data engineering environments.
Soft Skills & Operational Fit
The candidate demonstrates strong soft skills such as systematic problem-solving, collaboration, detail-orientation, adaptability, and a mentorship-driven approach. These are evident through their leadership in mentoring engineers, stakeholder collaboration, and focus on improving team productivity and delivery timelines. Their experience in standardizing reusable frameworks and automating processes indicates a strong operational fit for driving efficiency and reliability in data engineering teams.