Senior Data Engineer (ML/AI)

As a Senior Data Engineer, your role involves building a modern, cloud-native data platform on AWS and transitioning critical workloads from legacy systems to scalable, high-performing architectures. You should have strong hands-on expertise across AWS, Databricks, Spark, and SQL, along with experience in both legacy ETL tools and modern data lakehouse technologies. Key Responsibilities: - Design, build, and optimize data pipelines and ETL/ELT workflows using AWS services such as Glue, Redshift, S3, and Athena. - Migrate legacy data solutions (Siebel, Talend, Informatica) to modern AWS-native and lakehouse architectures. - Implement and manage data lake/lakehouse solutions using Apache Iceberg, Delta Lake, or Hudi. - Develop and optimize data warehousing solutions in Redshift, including data modeling and performance tuning. - Implement data quality and observability frameworks using Soda or similar tools. - Enable data governance and metadata management using platforms like Atlan, Collibra, or Alation. - Collaborate with cross-functional teams to deliver reliable and scalable data solutions. - Define and promote data engineering best practices and coding standards. - Explore and integrate AI/ML data engineering patterns where applicable. Qualifications Required: - 8+ years of experience in data engineering and large-scale data platforms. - Strong expertise in AWS services: Experience with open table formats such as Apache Iceberg, Delta Lake, or Hudi. - Hands-on experience migrating from legacy ETL tools (Siebel, Talend, Informatica). - Experience with data governance tools like Atlan, Collibra, or Alation. - Strong experience in data quality/observability tools like Soda. - Advanced SQL skills for complex queries and optimization. - Proficiency in Python for data engineering and Spark-based processing. - Experience with Apache Spark (EMR or Databricks). - Strong understanding of data modeling and data warehousing concepts. - Experience with Agile, DevOps, and CI/CD practices. - Exposure to AI/ML workloads is a plus. At Donyati, you will work in a culture that values curiosity, innovation, and impact while contributing to large-scale transformation programs. As a Senior Data Engineer, your role involves building a modern, cloud-native data platform on AWS and transitioning critical workloads from legacy systems to scalable, high-performing architectures. You should have strong hands-on expertise across AWS, Databricks, Spark, and SQL, along with experience in both legacy ETL tools and modern data lakehouse technologies. Key Responsibilities: - Design, build, and optimize data pipelines and ETL/ELT workflows using AWS services such as Glue, Redshift, S3, and Athena. - Migrate legacy data solutions (Siebel, Talend, Informatica) to modern AWS-native and lakehouse architectures. - Implement and manage data lake/lakehouse solutions using Apache Iceberg, Delta Lake, or Hudi. - Develop and optimize data warehousing solutions in Redshift, including data modeling and performance tuning. - Implement data quality and observability frameworks using Soda or similar tools. - Enable data governance and metadata management using platforms like Atlan, Collibra, or Alation. - Collaborate with cross-functional teams to deliver reliable and scalable data solutions. - Define and promote data engineering best practices and coding standards. - Explore and integrate AI/ML data engineering patterns where applicable. Qualifications Required: - 8+ years of experience in data engineering and large-scale data platforms. - Strong expertise in AWS services: Experience with open table formats such as Apache Iceberg, Delta Lake, or Hudi. - Hands-on experience migrating from legacy ETL tools (Siebel, Talend, Informatica). - Experience with data governance tools like Atlan, Collibra, or Alation. - Strong experience in data quality/observability tools like Soda. - Advanced SQL skills for complex queries and optimization. - Proficiency in Python for data engineering and Spark-based processing. - Experience with Apache Spark (EMR or Databricks). - Strong understanding of data modeling and data warehousing concepts. - Experience with Agile, DevOps, and CI/CD practices. - Exposure to AI/ML workloads is a plus. At Donyati, you will work in a culture that values curiosity, innovation, and impact while contributing to large-scale transformation programs.