Senior Data Engineer

• Design, develop, test, and maintain scalable ETL/ELT data pipelines
• Develop production-quality data-processing solutions using Python and PySpark
• Build reusable frameworks and components for ingestion, transformation, validation, and data publishing
• Process large structured, semi-structured, and distributed datasets
• Implement incremental and batch-processing patterns
• Design and develop scalable Snowflake data solutions, including SQL transformations, data models, views, and reusable data structures
• Optimize Snowflake workloads for performance, scalability, and cost
• Troubleshoot Snowflake performance and data-quality issues
• Build and maintain modular dbt transformation pipelines, tests, documentation, and deployment practices
• Design and implement Apache Iceberg data-lake and lakehouse solutions
• Work with partitioning, schema evolution, incremental processing, and table maintenance
• Integrate Iceberg datasets with Spark and AWS data-processing services
• Design and implement cloud-native AWS data solutions using services such as S3, Glue, EMR, and Lambda
• Implement secure AWS IAM access patterns, monitor workloads, and troubleshoot operational issues
• Diagnose and optimize Spark/PySpark jobs, SQL queries, Snowflake workloads, and data pipelines
• Implement automated data-quality checks, logging, monitoring, exception handling, and reconciliation
• Follow data security, governance, lineage, and access-control standards
• Write clean, modular, reusable, testable, and maintainable code
• Perform code reviews, implement testing, use Git and CI/CD, and maintain technical documentation
• Drive complex data-engineering requirements from design through production deployment
• Participate in architecture discussions and recommend implementation approaches
• Troubleshoot production and performance issues
• Mentor junior and mid-level data engineers
• Collaborate with Architects, Product Owners, Business Analysts, Data Scientists, QA, DevOps, and application teams
• Translate business and data requirements into scalable technical solutions
• Identify technical risks and recommend improvements

• 8+ years of overall Data Engineering experience
• Strong hands-on experience building enterprise-scale cloud data platforms and pipelines
• Strong hands-on expertise in Python, PySpark, Snowflake, dbt, Apache Iceberg, AWS, and advanced SQL
• Experience with data engineering, ETL/ELT, data lake/lakehouse architecture, and high-volume data processing
• Experience with Snowflake performance and cost optimization
• Experience with dbt models, tests, macros, documentation, and deployment practices
• Experience with Apache Iceberg partitioning, schema evolution, incremental processing, and table optimization
• Experience with AWS data services and secure access patterns using IAM
• Experience with Spark/PySpark optimization and troubleshooting
• Experience with data modeling and dimensional modeling
• Experience with data quality frameworks and automated validation
• Experience with Git-based version control, testing, code reviews, CI/CD, and production support
• Experience with data security, governance, lineage, and access controls
• Experience working in Agile delivery environments
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline is preferred
• AWS and/or Snowflake certification is an added advantage

• Remote work arrangement
• AWS and/or Snowflake certification is an added advantage