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 solid 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 contemporary 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