Data Pipeline Engineer

Summary

Join YipitData's dynamic Data Engineering team as a Data Pipeline Engineer. You will design, build, and maintain reliable data pipelines using Databricks, Spark, and AWS. This role involves collaborating with analysts and engineers to create scalable datasets, resolving performance bottlenecks, and contributing to improved data models and workflows. The position requires 3+ years of data engineering experience, proficiency in PySpark, Delta, and Databricks, and a Bachelor's or Master's degree in a related field. The role is remote, based in India, with some overlap required during training and onboarding with US working hours. YipitData offers a competitive salary and comprehensive benefits, including flexible work hours, vacation, 401K match, parental leave, and more.

Requirements

• You hold a Bachelor’s or Master’s degree in Computer Science, STEM, or a related technical discipline
• You have 3+ years of experience as a Data Engineer or in other technical functions
• You are excited about solving data challenges and learning new skills
• You have a great understanding of working with data or building data pipelines
• You are comfortable working with large-scale datasets using PySpark, Delta, and Databricks
• You understand business needs and the rationale behind data transformations to ensure alignment with organizational goals and data strategy
• You are eager to constantly learn new technologies
• You are a self-starter who enjoys working collaboratively with stakeholders
• You have exceptional verbal and written communication skills

Responsibilities

• Report directly to the Senior Manager of Data Engineering, who will provide significant, hands-on training on cutting-edge data tools and techniques
• Design, build, and maintain reliable data pipelines that power key products and analyses
• Detect and resolve performance bottlenecks using Spark UI, logs, and metrics
• Contribute ideas to improve data models, coding standards, and ETL workflows across the team
• Collaborate with analysts and other engineers to convert business requirements into scalable datasets
• Participate in code reviews, author clear documentation, and share learnings through demos and write-ups
• Continuously deepen your expertise in Databricks, Spark, and AWS by owning increasingly complex projects

Benefits

• Flexible work hours
• Flexible vacation
• A generous 401K match
• Parental leave
• Team events
• Wellness budget
• Learning reimbursement