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Data Engineer with 6+ years in Data Engineering & Cloud
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Assessing your cultural and operational fit
Senior Data Engineer with 6+ years of experience designing, building, and optimizing scalable Data Warehouse, ETL/ELT, and Cloud Data Engineering solutions. Expertise in Snowflake, Azure Data Factory, SSIS, SQL Server, and Data Modeling with a proven track record in on-premises to cloud migrations, performance tuning, and enterprise pipeline automation processing 2M+ records per day. Expanding into modern AI data engineering including Databricks, Delta Lake, RAG pipeline infrastructure, and LLM data layer support with vector databases.
Kongu Arts & Science College
Bachelor of Science (BSc) · Computer Science
N/A – June 30, 2018
Cognisive IT Services Pvt Ltd
Senior Data Engineer
September 1, 2019 – August 1, 2025
Coimbatore, Tamil Nadu, India
Supply Chain Data Integration & Real-Time Analytics – LogiTrack Solutions
June 24, 2026 – Present
Ingested high-velocity shipment and inventory data from ERP and WMS systems using SnowPipe for near-real-time loading into Snowflake. Designed and implemented a normalized Staging layer and a denormalized Reporting layer optimized for analytical query patterns. Built ADF pipelines to orchestrate multi-source data extraction from SQL Server, flat files, and REST APIs into Azure Blob Storage staging areas. Developed transformation logic in Snowflake using stored procedures to calculate key supply chain KPIs (on-time delivery rates, inventory turnover, lead times). Implemented slowly changing dimension (SCD Type 2) logic to track historical changes in supplier, product, and location master data.
AI Data Pipeline Exploration – Internal Innovation Lab
June 24, 2026 – Present
Explored Databricks environment by building basic PySpark transformation jobs on sample datasets, gaining hands-on familiarity with Delta Lake and notebook-based workflows. Set up a simple data ingestion pipeline from Azure Blob Storage into Databricks Delta tables, experimenting with incremental loads and schema enforcement. Assisted in preparing and chunking structured documents as part of an exploratory RAG proof-of-concept, understanding how clean data feeds into LLM retrieval pipelines. Loaded processed data into Snowflake from Databricks as part of a hybrid Lakehouse architecture experiment, connecting existing skills with the new stack. Self-studied LLM data engineering concepts including vector databases, embedding pipelines, and prompt logging to build awareness of AI-ready data infrastructure.
Retail Data Warehouse Modernization – SPS Commerce
June 24, 2026 – Present
Designed enterprise-level Data Warehouse architecture and Star Schema data models (Fact & Dimension tables). Developed SSIS packages for source data extraction, cleansing, and transformation. Built ADF pipelines for orchestration, scheduling, monitoring, and incremental data loads. Implemented watermark-based incremental load logic to capture only changed records. Migrated historical and real-time data from on-premises SQL Server to Snowflake with full audit trails.
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong alignment with the target role of Data Engineer, covering a broad spectrum of data engineering tasks from traditional ETL/DWH to modern cloud and AI-oriented data pipelines. The diversity of projects (retail, supply chain, internal innovation) and the proactive exploration of new technologies like Databricks and LLM data engineering suggest a growth mindset and a willingness to adapt to evolving industry demands, which are positive indicators for cultural fit in a dynamic technical environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to learning new technologies (e.g., self-studying LLM data engineering concepts) and a focus on delivering business impact (e.g., eliminating manual processes, improving reporting performance). The ability to work on diverse projects, from traditional DWH modernization to AI data pipeline exploration, suggests adaptability and a problem-solving mindset. The experience in production support and data validation indicates a commitment to operational excellence.