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Data Engineer with 4+ years in ETL orchestration and big data processing
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven Data Engineer with 3.6 years of experience in ETL orchestration, data pipeline automation, and big data processing. Proficient in PySpark, Databricks, SQL, Python, Delta Lake, and Azure Data Factory (ADF). Skilled in data modeling, data warehousing, and performance tuning to drive actionable business insights.
Mahatma Gandhi Kashi Vidyapith, Varanasi
M.A in Art · Art
August 1, 2017 – June 30, 2017
Mahatma Gandhi Kashi Vidyapith, Varanasi
B.A. in Art · Art
August 1, 2015 – June 30, 2015
CSM, Odisha
Customer Transactions Analytics
December 1, 2023 – Present
India
Bizzsetu, Maharashtra
Software Engineer [D.E.]
December 1, 2021 – December 1, 2023
India
Master's in Data Science with Power BI – 9-month Intensive Industry Program
Console Flare
June 1, 2026 – Present
Azure Data Fundamentals
Microsoft Certified
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's experience across two companies, Bizzsetu and CSM, shows adaptability to different organizational contexts. The focus on improving data accessibility, accuracy, and reducing latency aligns with a performance-driven culture. The breadth of skills, including various Azure services, PySpark, and data engineering concepts, indicates a willingness to learn and apply diverse technologies, which is positive for cultural fit in an evolving tech environment. The educational background in Art, followed by a career transition into Data Engineering, suggests a unique perspective and strong self-motivation.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as Agile Development, Collaboration, Teamwork, and Problem-Solving, which are crucial for operational fit in a data engineering role. The experience descriptions suggest a results-driven individual focused on efficiency and accuracy, aligning well with typical project delivery expectations.