Senior Data Engineer

Unseeen is seeking a Senior Data Engineer to design, build, and maintain scalable data platforms and pipelines that power analytics, reporting, and data-driven decision-making across the organization. The role involves transforming raw data into reliable datasets used by various teams and optimizing data performance and quality.

Responsibilities:

• Design, develop, and maintain scalable, reliable, and efficient data pipelines and ETL/ELT processes
• Build and optimize data models, data warehouses, and data lakes to support analytics and business intelligence use cases
• Collaborate closely with Analytics, Data Science, Product, and Engineering teams to understand data requirements and deliver trusted datasets
• Ensure data quality, accuracy, availability, and consistency through validation, monitoring, and testing
• Optimize data performance, cost, and scalability across storage and processing systems
• Implement and enforce data engineering best practices, including documentation, version control, and code reviews
• Support real-time and batch data processing use cases
• Identify and resolve data pipeline failures, performance bottlenecks, and reliability issues
• Work with DevOps and Platform teams to improve CI/CD workflows, automation, and infrastructure-as-code for data systems
• Contribute to the evolution of the organization’s data architecture and long-term data strategy

Requirements:

• Strong experience building and maintaining production-grade data pipelines
• Proficiency in SQL and at least one programming language such as Python, Java, or Scala
• Experience with data warehousing and analytics platforms (e.g., Snowflake, BigQuery, Redshift)
• Solid understanding of data modeling, schema design, and performance optimization
• Experience with batch and streaming data processing frameworks
• Familiarity with REST APIs, data integrations, and third-party data sources
• Strong problem-solving skills and attention to data quality and reliability
• Excellent communication and collaboration skills
• 5–8+ years of professional experience in data engineering or related roles
• Experience in SaaS, cloud-native, or data-driven product organizations
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP
• Experience with tools such as Airflow, dbt, Spark, Kafka, or similar technologies
• Exposure to DevOps practices, CI/CD pipelines, and infrastructure-as-code
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience