📍 Dallas, TX
📧 karthik.preeni@gmail.com
🔗 https://linkedin.com/in/karthikeyanpalanikumar
I am a Product Analyst with a strong focus on Data Engineering and Analytics Engineering. I build scalable data pipelines, design dimensional data models, and deliver analytics platforms using modern cloud and data stack technologies.
- 5+ years of experience in data analytics, BI, and data engineering workflows
- Strong hands-on experience with Snowflake, dbt, AWS, and modern ELT pipelines
- Experienced in building scalable data pipelines, automating ETL workflows, and designing data models
- Delivered high-impact dashboards and analytics solutions used by product and business teams
- Snowflake, dbt, ETL/ELT, Data Warehousing
- Data Modeling (Star Schema, Dimensional Modeling)
- CDC, Data Pipelines, Process Automation
- Data Quality, Testing, and Observability
- AWS (S3, Lambda, Glue, Kinesis, DMS)
- Azure Data Factory, Azure Synapse
- Databricks
- Python
- SQL (T-SQL, PL/pgSQL)
- Power BI (DAX, Dataflows, Incremental Refresh)
- Tableau
- Streamlit
- SQL Server (SSIS, SSAS)
- PostgreSQL
- MySQL
- GitHub, GitLab
- Azure DevOps, JIRA, Confluence
- Jupyter Notebook, VS Code
OneSource Virtual, TX | Feb 2022 – Present
- Built 15+ Power BI dashboards, improving accessibility for product teams and reducing manual reporting by 80%
- Developed ETL pipelines using Azure Data Factory and SQL Server to process 500K+ records daily
- Optimized data pipelines, reducing query execution time by 40%
- Processed millions of tax records using MySQL and Power BI with incremental refresh
- Built campaign analytics pipelines using Python APIs (HubSpot, Mailchimp) processing 2.7M+ records
Microsoft, TX | Oct 2022 – Mar 2023
- Optimized Power BI dashboards, reducing refresh time by 50%
- Improved data models and DAX logic for performance and scalability
- Streamlined ETL pipelines using Azure Data Factory
- Contributed to CI/CD and Agile workflows using Azure DevOps
Samsung Electronics America, TX | Jul 2021 – Jan 2022
- Automated reporting workflows using Python and SQL, reducing manual effort by 85%
- Built anomaly detection systems auditing 100+ reports weekly
- Ensured 99% data accuracy across multi-region datasets
- Built event-driven pipeline using API Gateway, Lambda, Kinesis, and Snowpipe
- Enabled low-latency ingestion and real-time analytics
- Built automated ELT pipeline with S3 ingestion and Snowflake modeling
- Designed dbt models with testing and documentation
- Implemented SCD Type 1 using Streams, Tasks, and stored procedures
- Implemented SCD Type 2 using dbt snapshots and versioned modeling
- Built full load + CDC pipeline from RDS to S3
- Ensured continuous replication and change tracking
- Advanced Data Engineering
- Analytics Engineering (dbt + Snowflake)
- Scalable Data Pipelines
- Data Platform Design
- Building production-ready data systems