Data Engineer

Role Overview

As a Data Engineer at Nineleaps, you will design, build, and maintain scalable data pipelines that power reliable analytics and business-critical decisions. You'll work across large-scale distributed data ecosystems, manage ETL/ELT workflows, and ensure high availability and performance of maintained datasets. This role is ideal for professionals who enjoy solving complex data challenges, optimizing pipelines, and building robust systems that support data-driven products and platforms.

Key Responsibilities

• Build, maintain, and optimize large-scale data pipelines and processing frameworks
• Work within big data distributed ecosystems such as Hadoop and Hive
• Write and optimize complex queries using HQL and PrestoQL, including aggregations and performance tuning
• Design, develop, and deploy high-volume ETL/ELT pipelines for complex and near real-time data collection
• Maintain high service reliability for managed datasets, ensuring Tier 1 and Tier 2 SLAs remain above 99%
• Support and maintain SLA commitments for Tier 3 datasets and tables
• Work with data management teams and project leads to deliver scalable and reliable data solutions
• Ensure data workflows are efficient, resilient, and aligned with evolving business requirements
• Contribute to improving data quality, performance, and operational excellence across data systems
• Participate in on-call support for critical data pipelines and maintained datasets

What We're Looking For

• 3–6 years of experience in Data Engineering
• At least 3 years of hands-on experience working with SQL, Python, and big data tools
• Strong experience with Hadoop, Hive, and distributed data ecosystems
• Excellent knowledge of HQL and PrestoQL, including query optimization, complex aggregations, and performance tuning
• Experience building and maintaining data processing frameworks and big data pipelines
• Solid understanding of data warehouse architecture, ETL/ELT processes, and data structures
• Working knowledge of Python for ETL and data engineering workflows
• Ability to communicate data insights, methods, and outcomes clearly with peers and stakeholders
• Strong problem-solving skills and attention to quality, performance, and reliability
• Ability to multitask and work effectively in collaborative team environments
• Comfortable working with remote teams across time zones
• A continuous improvement mindset with the ability to raise the bar for quality and efficiency within the team