Data Engineer with 4+ years in Cloud Data Pipelines & ETL
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Assessing your cultural and operational fit
Data Engineer with about 4 years of experience building and improving data pipelines. Skilled in using cloud tools like DBT, AWS, and GCP to create reliable data pipelines. Focused on supporting data-driven decisions and following best practices in data engineering.
Rajiv Gandhi Proudyogiki Vishwavidyalaya
B.E. · Computer Science Engineering
August 1, 2014 – June 30, 2018
Aretove
Senior Data Engineer
September 1, 2024 – Present
Pune, Maharashtra, India
Coditas
Data Engineer
August 1, 2022 – September 1, 2024
Pune, Maharashtra, India
AWS Certified Data Analytics - Specialty
AWS
July 1, 2023 – July 1, 2026
Cultural Fit Analysis
The candidate has experience across two companies, demonstrating stability and growth from Data Engineer to Senior Data Engineer. The projects involve diverse data sources and business applications (marketing, operations, sales), suggesting an ability to adapt to different business contexts. The use of various modern data tools (Airbyte, dbt, Airflow, Snowflake, BigQuery, AWS Glue, GCP Cloud Functions) indicates a willingness to learn and apply new technologies, which aligns well with a dynamic, innovation-driven culture. The certification in AWS Data Analytics further supports a proactive approach to skill development.
Soft Skills & Operational Fit
The candidate's resume indicates a focus on practical problem-solving in data engineering, including error handling and efficient workflow orchestration. The descriptions suggest an ability to work independently on pipeline development and data model creation. The experience with various cloud platforms (AWS, GCP) and tools (Airbyte, Airflow, dbt) points to adaptability and a broad operational skillset. The emphasis on generating insights for business decisions (e.g., targeting customers) shows a results-oriented approach.