Karthinivash S R

Data Engineer with 3 years of hands-on experience designing cloud-native data platforms on Snowflake and AWS, building scalable ELT/ETL pipelines with Python, SQL, PySpark, Airflow, and DBT Cloud across structured and semi-structured sources. Proven track record in performance-tuning distributed Spark workloads, delivering analytics-ready dimensional models (star schema, medallion architecture), data virtualization via Snowflake Data Sharing, and driving data quality and governance at scale. Currently pursuing an MS in DS&ML with applied exposure to RAG systems and vector search — bridging modern data engineering with AI-ready platform design.

Data Engineer at Thoughtworks (2022-05 – Present)

Enterprise Data Engineering · Workforce Analytics · Compliance & Operations Reporting

• Designed and delivered scalable, cloud-native data platforms on Snowflake and AWS powering workforce analytics, compliance monitoring, and executive BI reporting for large enterprise clients.
• Architected end-to-end ELT/ETL pipelines using Python, SQL, Airflow (AWS MWAA), and dbt Cloud — ingesting structured and semi-structured data from REST APIs, SFTP, relational databases, and S3.
• Designed schema-on-read ingestion framework that auto-adapted to 30+ evolving upstream fields, eliminating ~12 hours/month of manual schema-fix intervention.
• Diagnosed an 8-hour Spark job bottleneck caused by data skew; restructured partitioning and rewrote the execution plan — cutting runtime to 85 minutes and reducing compute costs by ~30%.
• Built analytics-ready dimensional models (star schema, fact-dimension design) using advanced SQL — window functions, CTEs, multi-table joins, and incremental loading strategies.
• Established data reliability frameworks: schema validation, automated quality checks, reconciliation logic, audit logging, IAM access control, and SLA adherence monitoring.
• Automated CI/CD-aligned data workflows using AWS MWAA and dbt Cloud — enabling maintainable, version-controlled pipelines across distributed teams.
• Consulted business stakeholders; translated ambiguous requirements into analytics-ready datasets in Agile, cross-vendor environments.
• Mentored 3–5 junior engineers through code reviews, SQL pattern libraries, and architecture guides — reducing onboarding time by ~2 weeks per new hire.
• Contributed to scaling the account team from 2 to 21 engineers over 3 years through architecture decisions, onboarding standards, and technical leadership.
• Leveraged Generative AI (LLM-based) tooling to accelerate technical documentation and automation workflows, improving engineering delivery efficiency by ~85%.

MS in Data Science & Machine Learning – Woolf University (2024)

BE/B.Tech in Computer Science & Engineering – Bannari Amman Institute of Technology (2018 – 2022)