Job Responsibilities

• Build data pipelines for batch and real-time processing with strong data quality. Ensure systems are performant, reliable, scalable, and cost-efficient
• Model data based on how it will be accessed and the capabilities of underlying systems. Ensure data is accessible, stored in appropriate formats, and integrates seamlessly with analytics and machine learning platforms
• Write design proposals that explain system architecture, decision trade-offs, and long-term considerations. Maintain documentation for data pipelines and datasets, including operational runbooks and user guides
• Analyze data to understand business patterns and extract insights. Develop applications that present findings in clear, actionable formats
• Monitor data pipelines to meet service level agreements. Diagnose and resolve production issues promptly
• Collaborate with Data Scientists, Product Managers, and DevOps teams to understand requirements and deliver effective data solutions. Contribute to technical discussions on system design and data governance
• Mentor junior engineers in developing their technical skills. Foster a collaborative environment focused on continuous learning and quality delivery. Ensure adherence to coding standards and efficient resource utilization

Job Qualifications :

Technical Skills:

• 5+ years of experience in Data Engineering, with a proven track record of designing, building, and maintaining large-scale data pipelines
• Strong experience in big data processing frameworks like Apache Spark and table formats like Apache Iceberg
• Proficient in SQL and experience with data modeling for analytical databases like Redshift/BigQuery/Snowflake/Star Rocks
• Experience with AWS services (e.g., Redshift, Athena, Glue, S3, EMR) or other similar systems from other Cloud Providers
• Proficient in one high-level language like Java, Scala or Python
• Experience with Machine Learning frameworks like Sage Maker or MLFlow will be an advantage

Soft Skills:

• Strong problem-solving abilities with large-scale distributed systems—identifying issues, optimizing performance, and resolving complex technical challenges
• Effective communication skills across all levels, translating technical concepts for both engineering teams and business stakeholders
• A sense of ownership and accountability, with a track record of delivering results and following through on commitments
• Curiosity about the business context, asking the right questions to understand real problems before building solutions