Senior Data Engineer

• Design, build, and optimize production data pipelines and data platforms on AWS
• Build scalable data processing workloads using Spark/PySpark, Python, and SQL
• Build and modernize data lakes and lakehouse architectures using Amazon S3, Apache Iceberg, AWS Glue, and related services
• Design, troubleshoot, and optimize analytical workloads and data warehouses, including Amazon Redshift and other OLAP platforms
• Modernize legacy and inefficient data workloads by moving ETL/ELT processing to scalable distributed data architectures
• Work with streaming and event-driven data pipelines using Kafka, Kinesis Data Firehose, and related services
• Investigate production data problems and recommend practical solutions
• Own customer projects end-to-end, from understanding technical problems through implementation, testing, and delivery
• Work directly with customers to clarify requirements, explain technical decisions, and recommend solutions
• Build data foundations for analytics and ML workloads with Data Scientists, DevOps, and MLOps engineers
• Contribute to dashboards and analytics using QuickSight or other BI tools
• Apply engineering practices around data quality, testing, CI/CD, security, governance, and infrastructure automation

• Strong production experience in Data Engineering, with the ability to independently own and deliver data projects
• Deep hands-on experience with Apache Spark / PySpark and distributed data processing
• Strong Python and SQL skills
• Hands-on experience building data solutions on AWS
• Strong understanding of data lakes, lakehouse architectures, ETL/ELT, data modeling, and data processing at scale
• Experience with analytical databases and data warehouses
• Strong Amazon Redshift experience is highly valuable; deep experience with Snowflake, Synapse, or similar OLAP technologies is also relevant
• Understanding of streaming architectures and technologies such as Kafka or Amazon Kinesis
• Ability to troubleshoot existing data systems, understand upstream and downstream data flows, and improve performance and reliability
• Ability to work autonomously, make pragmatic technical decisions, and take responsibility for delivery
• Good communication skills and ability to discuss technical problems and solutions directly with customers in English
• Experience with Apache Iceberg or other modern open table formats and lakehouse technologies
• Amazon OpenSearch Service / Elasticsearch experience
• Experience with BI and visualization tools such as Amazon QuickSight, Tableau, or Power BI
• Terraform and Infrastructure as Code
• CI/CD practices for data pipelines, including data testing and validation
• Experience preparing data platforms for ML workloads using services such as Amazon SageMaker
• Exposure to Amazon Bedrock, GenAI, or agentic development
• Experience with Databricks, Snowflake, ClickHouse, or other modern data platforms