AWS Data Engineer

Job description

**About the Company** We're looking for a Senior Data Engineer to design, build, and support scalable cloud-native data platforms on AWS — someone equally comfortable in the weeds of a pipeline and thinking through system-level architecture. You'll own production-grade batch and streaming pipelines, modern lakehouse architectures, and reliable ETL/ELT solutions, working closely with engineering, analytics, and infrastructure teams to ship secure, scalable, high-performing data solutions. **About the Role** Key Responsibilities - Design, develop, and maintain batch and streaming data pipelines on AWS, with a strong eye toward end-to-end system design and reliability. - Build scalable ETL/ELT workflows using AWS Glue, PySpark, and SQL. - Develop event-driven ingestion solutions using Lambda, SQS, API Gateway, or EventBridge. - Design and optimize lakehouse architectures using Amazon S3 and modern table formats (e.g., Apache Iceberg). - Implement secure data access using IAM, Lake Formation, and AWS best practices. - Build reliable data processing with monitoring, logging, retry mechanisms, and data quality checks. - Build and support streaming solutions using Kafka, Amazon MSK, Kinesis, or similar. - Collaborate cross-functionally to deliver scalable, well-architected data platforms — occasionally partnering on infra (Terraform/CloudFormation, containers) where pipelines meet platform. **Qualifications** - Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience). - Proven experience designing, building, and supporting production data pipelines. - Strong analytical, problem-solving, and communication skills. **Required Skills** - 5+ years in Data Engineering with strong AWS expertise and solid system design fundamentals. - Hands-on with Glue, Lambda, S3, Athena, IAM, SQS, DynamoDB, CloudWatch, EMR, and ECR. - Strong SQL and PySpark skills building production ETL/ELT pipelines. - Experience with data lake/lakehouse architectures and Apache Iceberg (or similar table formats). - Experience with Kafka, Amazon MSK, Kinesis, or equivalent streaming platforms. - Strong understanding of data modeling, dimensional modeling, and data quality principles. - Experience troubleshooting distributed systems and optimizing production data pipelines at scale. **Preferred Skills** - AWS Lake Formation - Terraform or CloudFormation, Docker and containerized workloads - CI/CD (GitHub Actions, Jenkins, GitLab CI) - Data observability and quality frameworks