AWS Data Engineer

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