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.
- Robust 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

📌 AWS Data Engineer (Bengaluru)
🏢 Brillio
📍 Bengaluru