Raghavendra H S

About Raghavendra H S

Data Engineering Leader with 14+ years of experience building scalable data platforms and leading distributed teams. Currently managing two data engineering squads in India and collaborating with engineering managers and leads for two additional squads in the US. I own engineering deliverables, drive sprint planning, and partner with product managers, directors, and principal engineers to align technical strategy with business goals.Expert in Python, Spark, AWS, Databricks, Airflow, and EMR, with a proven track record in designing robust data infrastructure, optimizing pipelines, and ensuring data quality. Skilled at balancing technical leadership with people management—mentoring engineers, fostering cross-team collaboration, and driving architecture discussions for enterprise-scale platforms.Key achievements include replatforming 200+ pipelines, leading Databricks upgrades, improving throughput by 40%, and delivering $350K/year in cloud savings through EMR optimizations and Graviton migrations. Certified in Databricks Data Engineering, AWS Big Data Specialty, and CCA 175.Passionate about building high-performing teams and architecting solutions that enable data-driven decision-making at scale.

Experience

1. Lead Data Engineer - Scpt Data Foundation Platform

   Nike

   Oct 2022 — Present · Bengaluru, IN

   I lead two Data Engineering squads (14–16 engineers) responsible for Supply Chain & Planning data platform modernization, reliability, and cost optimization. I combine hands-on architecture with senior technical leadership to drive Lakehouse strategy, cross-team design, and delivery.Key Achievements:• Led Databricks Lakehouse modernization and replatformed 200+ pipelines from EMR to Databricks (Unity Catalog), standardizing patterns and reducing operational complexity.• Drove evaluation and adoption of Databricks Serverless for selected pipelines to reduce management overhead and improve cost/performance tradeoffs.• Directed Databricks upgrade v9.1 → v14.3 LTS and EMR upgrades with zero production defects.• Delivered >$150K/year compute savings via cluster right-sizing and Graviton migration and $45K/year storage savings through lifecycle policies and partitioning (part of >$350K/year modernization impact).• Designed and rolled out a Spark-based observability & data-quality framework (Spark Expectations) with reusable alerting and dashboards, improving data reliability by ~40% and reducing incident MTTR.• Implemented Spark tuning, Z-Ordering, Liquid Clustering and architectural simplifications that reduced pipeline runtimes by ~43%.• Acted as principal-level technical lead across teams: influenced platform design strategy, led architecture discussions with principal data engineers, and resolved cross-team technical tradeoffs.• Built a centralized pipeline framework and YAML/templates for faster onboarding; introduced AI-assisted development tools (GitHub Copilot, Cursor AI) to increase delivery velocity by ~25%.• Hired, mentored, and enabled 18+ engineers; partnered with Product, SRE, and data consumers to align platform capabilities with SLAs and business KPIs.Certifications:• Databricks Certified Data Engineer Associate• Databricks Certified Associate Developer for Apache Spark

Education

• VTU

  Bachelor of Engineering (B.E.), Electrical, Electronics and Communications Engineering

  2007 — 2011

Skills

• Mockito
• Junit
• Oracle
• Requirements Analysis
• Unix
• C
• Automated Software Testing
• Sdlc
• Performance Tuning
• Unix Shell Scripting
• Sybase
• Xml
• Sybase Adaptive Server
• Ibm Aix
• Clearcase
• Testing
• Java
• Github
• Software Project Management
• System Deployment
• Couchbase
• Oracle Pro*C
• Red Hat Linux
• Agile Methodologies
• Trello
• Requirements Gathering
• Sql
• Software Development Life Cycle (Sdlc)
• Pl/Sql
• Shell Scripting
• Jira
• Jenkins
• Oracle Reports
• Cucumber
• Software Design
• Oracle Sql Developer
• Oracle Forms