Harsh Ojha

About Harsh Ojha

I am a Data Analyst / Analytics Engineer with hands-on experience in building high-performance, scalable data pipelines and transforming raw data into actionable insights for businesses. I specialize in designing ETL workflows, optimizing analytics systems, and creating data platforms that scale for startups and enterprise environments alike.Over the years, I have:•Led the development of large-scale ETL pipelines using Apache Spark, reducing execution time by up to 70%.•Optimized data workflows with in-memory computation, caching, and distributed processing, enabling near-real-time analytics.•Designed scalable database architectures, applying indexing, partitioning, ER modeling, and query optimization to improve performance by 2–4×.•Built and managed virtual machines on bare-metal hardware, gaining expertise in Linux systems, OS configuration, memory partitioning, and networking fundamentals.•Created interactive dashboards and reports in Metabase, improving business visibility and report responsiveness by 3×.•Achieved 30–40% reduction in infrastructure and query costs through optimized pipelines, partitioning, and compression strategies.I thrive at the intersection of data engineering and analytics, bridging technical solutions with business decisions. My experience spans distributed ETL systems, cloud platforms, database design, and performance optimization, making me a strong partner for startups and global teams looking to scale their data infrastructure and derive meaningful insights faster.I’m passionate about creating systems that turn complex data into actionable insights while improving efficiency, scalability, and cost-effectiveness.

Experience

1. Data Analyst

   AppSquadz

   May 2025 — Present · Noida, IN

   Roles, Responsibilities & Achievements-•Led the design and optimization of large-scale ETL pipelines using Apache Spark for high-volume analytical datasets.•Reduced ETL execution time by 60–70% by migrating batch workflows to Spark and tuning executors.•Improved query performance by 50%+ using in-memory computation, caching, optimized joins, and partition-aware processing.•Worked extensively with Spark driver–executor (master–worker) architecture to efficiently distribute workloads.•Enabled near-real-time analytics by reducing data processing latency from hours to minutes.•Cut infrastructure load by ~40% through optimized Spark resource utilization and VM-level tuning.•Created and managed virtual machines on bare-metal hardware, improving hardware utilization efficiency by ~45%.•Configured Linux environments, including OS installation, disk partitioning, memory management, and CPU allocation.•Built strong fundamentals in networking (IP addressing, NAT, bridging, routing) through VM and hypervisor setup.•Designed scalable database schemas and architectures focused on performance and maintainability.•Implemented logical table grouping using prefixes to improve database organization and clarity.•Reduced database query execution time by 2–4× through effective indexing, partitioning, and schema optimization.•Created and maintained ER diagrams to ensure accurate data modeling and relationships.•Achieved 30–35% reduction in storage and query costs through optimized partitioning and compression.•Improved dashboard and report responsiveness by 3×, enhancing stakeholder experience.•Decreased recurring data quality and performance issues by ~25% through improved data modeling and query optimization.

Education

• ABESIT

  Bachelor of Technology - BTech, computer science data science

  2020 — 2024