7 days ago
Noida, IndiaStaff+
Responsibilities
- Design and develop backend services, data APIs, and serverless components for analytics and application workloads.
- Build event-driven architectures using S3, EventBridge, SNS/SQS, Lambda, Step Functions, and AWS Glue for batch and near-real-time ETL.
- Develop AWS Glue jobs, workflows, and crawlers to load, transform, and curate data in Amazon Redshift and related data stores.
- Model, optimize, and manage analytical data structures, including dimensional schemas, partitioning, and distribution and sort keys.
- Support Amazon QuickSight data preparation, semantic modeling, row-level security, dashboards, analyses, and performance tuning.
- Apply scalable big data storage and compute practices using data lakes, columnar formats, pushdown, and partition pruning.
- Implement monitoring, logging, and alerting for serverless and event-driven workloads using CloudWatch, X-Ray, and related services.
- Collaborate with data engineers, data scientists, and product teams to translate business and AI/analytics requirements into technical solutions.
- Contribute to architecture decisions, design reviews, AWS best-practice adoption, security, IAM, networking, cost optimization, and reliability.
- Mentor engineers in serverless, data, AI application patterns, coding standards, and cloud-native design.
Requirements
- At least 10 years of hands-on experience building applications and services on AWS, ideally as a cloud application architect or backend engineer.
- Advanced Python proficiency for backend development, ETL, automation, AWS Lambda, and AWS Glue jobs.
- Experience with AWS serverless computing, including Lambda, API Gateway, Step Functions, and event-driven integration patterns.
- Experience designing and maintaining ETL pipelines and data workflows with AWS Glue and related data integration tools.
- Strong SQL skills and experience with analytical databases and data warehouses, especially Amazon Redshift.
- Knowledge of dimensional modeling, fact and dimension design, slowly changing dimensions, partitioning, and indexing.
- Experience with AWS big data workloads, including S3 data lakes, Parquet or ORC, Redshift Spectrum, Athena, or similar technologies.
- Hands-on experience with BI and data visualization tools, preferably Amazon QuickSight, including dataset design, dashboards, and security.
- Understanding of AWS infrastructure fundamentals, including VPCs, subnets, networking, IAM, encryption, and security best practices.
- At least one relevant AWS certification, such as Solutions Architect, Data Analytics, or Developer, or equivalent practical expertise.
Benefits
- Competitive total rewards package, continuing education and training, and growth potential within a worldwide organization.
- Remote or hybrid work arrangement.
