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Data Engineer with less than a year in Data Engineering & Cloud
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Motivated and detail-oriented fresher Data Engineer with hands-on project experience in building end-to-end data pipelines using Snowflake, SQL, and Python. Proficient in Medallion Architecture (Bronze–Silver–Gold), ETL/ELT processes, and data warehousing concepts including star schema design, Snowpipe, and AWS S3 integration. Passionate about building scalable, analytics-ready data solutions and eager to contribute to a high-impact data engineering team.
Dr. A.P.J. Abdul Kalam Technical University, Lucknow
B.Tech · Computer Science
November 1, 2020 – July 1, 2024
Jan Bharti Inter College, Khangi Chak, Ghazipur
Intermediate
June 1, 2018 – May 31, 2019
Medallion Architecture Data Pipeline (Bronze–Silver–Gold)
June 24, 2026 – Present
Designed and implemented a production-grade end-to-end data pipeline using Medallion Architecture (Bronze, Silver, Gold) layers in Snowflake, enabling scalable and analytics-ready data processing. Data Source — Where the raw data originates: Raw CSV and JSON files stored in AWS S3 buckets (simulating transactional/operational system exports). Data types included sales transactions, customer records, and product master data. AWS S3 external stages configured in Snowflake to connect cloud storage as the pipeline entry point. Bronze Layer (Raw Ingestion) — Where data lands first: Ingested raw files from AWS S3 into Snowflake Bronze tables using COPY command for bulk loads and Snowpipe for automated, event-driven continuous loading. Data stored as-is with no transformation — preserving original structure, schema, and values for full auditability and reprocessing. Schema-on-read approach applied; all raw records retained including duplicates and nulls. Silver Layer (ELT Transformations) — Where ELT is performed: Executed ELT transformations entirely within Snowflake using advanced SQL — leveraging the warehouse compute power for scalable in-place transformation. Performed data cleaning: removed duplicates using ROW_NUMBER() window functions, handled NULL values, and standardized date/string formats. Applied schema validation to enforce data types and reject non-conforming records. Implemented incremental loading logic using watermark columns (e.g., updated_at timestamp) to process only new/changed records, reducing compute cost. Automated data quality checks at this layer to validate completeness, accuracy, and referential integrity before promotion to Gold. Gold Layer (Business-Ready) — Where analytics-ready data is served: Modeled clean Silver data into a Star Schema — created Fact tables (e.g., fact_sales) and Dimension tables (e.g., dim_customer, dim_product, dim_date) aligned to business reporting requirements. Built aggregated and pre-joined datasets optimized for BI tools and dashboards (reports on revenue, customer trends, product performance). Optimized Snowflake queries for performance and cost efficiency using clustering keys and result caching.
Data Warehouse Design (Snowflake)
June 24, 2026 – Present
Designed a structured data warehouse in Snowflake from scratch, defining schemas and data models aligned with business requirements. Loaded structured data from CSV/JSON files using COPY command and Snowpipe, ensuring efficient and reliable ingestion. Created normalized fact and dimension tables and performed SQL-based transformations to produce analytics-ready datasets. Implemented data quality checks to validate completeness, accuracy, and consistency across datasets.
Python with Machine Learning Workshop
Innowitt Global Pvt Ltd, Lucknow
May 1, 2022 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a clear alignment with a Data Engineer role, focusing on core data pipeline and warehousing skills. The breadth of technologies used (Snowflake, SQL, Python, AWS S3, dbt, Git) shows a willingness to learn and apply diverse tools. As a fresher, the project diversity is good, but the lack of professional experience limits the assessment of cultural fit in a team environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a detail-oriented and motivated individual. The focus on production-grade pipelines, data quality checks, and query optimization suggests an operational mindset. However, as a fresher with no professional experience, direct evidence of stress handling, team collaboration, or advanced problem-solving in a corporate setting is not available.