Krishna Deepika

📊 My first script was probably a [SELECT * FROM life] — and I’ve been optimizing ever since…

What started as a curiosity for “how data flows behind the scenes” during my undergrad years quickly evolved into a full-blown passion for data engineering. Courses in databases, big data, and cloud computing sparked my interest — but it was the thrill of building scalable systems and optimizing pipelines that truly hooked me.

Eventually I took the first step into the journey of data with a Data Analyst internship, where I first saw the power of transforming messy raw data into real business impact. That internship turned into a full-time role, and over the next few years, I immersed myself in SQL-heavy reporting, building dashboards, and automating analytics pipelines to improve efficiency and insights across domains.

Driven by a deeper curiosity and a love for math, I pursued a Master’s in Data Science and Business Analytics. During grad school, I interned again — this time focused on cloud data engineering — and I knew I had found my niche. I loved the challenge of building systems that weren’t just analytical but also scalable, automated, and built for real-time decision-making. Currently, I’m a Data Engineer at Acer America, where I’ve:

• Designed ETL pipelines that process 1.2TB+ of data weekly
• Automated 85+ Airflow jobs across departments
• Worked closely with data scientists to deploy ML models that improved failure prediction accuracy by 23%
• Supported real-time analytics for over 5 million customer records across Snowflake, Redshift, and AWS/GCP stacks

☁️ Cloud & Tools: Snowflake | Redshift | BigQuery | dbt | Kafka | SageMaker | Power BI

Outside work, I’m passionate about sustainability. I research the environmental impact of recycled and organic waste, enjoy gardening, and love creating homemade eco-friendly products — it’s my way of balancing tech with the earth 🌱

If you’re building something impactful or just want to talk data or sustainability — I’d love to connect!

• 4+ years of end-to-end experience in data engineering and analytics, transitioning from Data Analyst Intern in India to a full-time Data Engineer role in the U.S.
• Hands-on expertise building ETL pipelines using Apache Spark, AWS Glue, and Python, processing over 1.2TB/week and supporting analytics for 5M+ customer records.
• Designed and optimized SQL-heavy data transformations using Teradata SQL, T-SQL, and Snowflake, enabling robust BI outputs and advanced reporting layers.
• Automated 85+ data workflows via Apache Airflow, simulating Control-M-style job orchestration to reduce manual intervention by over 90%.
• Collaborated with data scientists to deploy ML models (AWS SageMaker), improving predictive accuracy and integrating insights into business processes.
• Conducted data cleaning, validation, and root cause analysis using Python, Pandas, Jupyter, and Power BI, supporting inventory, marketing, and customer data strategies.
• Skilled in cloud-native data engineering across AWS (Redshift, S3, Lambda), Azure (Data Factory, Blob), and Google Cloud (BigQuery, Dataflow, Pub/Sub).
• Additional technologies: dbt, Kafka, Docker, Kubernetes, Terraform, SSIS, Looker, Tableau, Power BI, Notion, JIRA, Confluence.

🎓 Master of Science in Data Science and Business Analytics – University of North Carolina at Charlotte

🎓 B.Tech in Engineering – SRM University, India

🌏 AI & ML Winter Program – Asia University, Taiwan

Certifications:

✅ Microsoft Certified: Azure Data Engineer Associate

✅ Machine Learning in Production – Coursera

✅ Machine Learning/AI Intern – Microsoft Verzeo

✅ Python (Crash, Intermediate, Advanced) – Coursera