to design, build, and optimize large-scale data platforms on AWS. The ideal candidate will have deep hands-on experience with AWS data services, batch and streaming data processing, and orchestration frameworks, playing a key role in architecting and maintaining enterprise-grade data solutions.
Key Responsibilities
Design, develop, and optimize scalable data pipelines using AWS native services
Lead the implementation of batch and near-real-time data processing solutions
Architect and manage data ingestion, transformation, and storage layers
Build and maintain ETL/ELT workflows using AWS Glue and Apache Spark on EMR
Orchestrate complex data workflows using Apache Airflow (MWAA)
Develop and manage serverless data processing using AWS Lambda
Design and optimize data warehouses using Amazon Redshift
Implement and manage NoSQL data models using Amazon DynamoDB
Utilize AWS DataZone for data governance, cataloging, and access management
Monitor, log, and troubleshoot data pipelines using Amazon CloudWatch
Ensure data security, quality, performance, and cost optimization
Collaborate with architects, analytics teams, and business stakeholders
Provide technical guidance and mentorship to junior team members
Required Skills & Experience
8+ years of experience in data engineering and data platform development
Strong hands-on experience with:
+ AWS Glue
+ Amazon EMR (Spark)
+ AWS Lambda
+ Apache Airflow (MWAA)
+ Amazon EC2
+ Amazon CloudWatch
+ Amazon Redshift
+ Amazon DynamoDB
+ AWS DataZone Strong experience with batch data processing architectures
Proficiency in SQL, Python, and Spark
Experience working with large-scale datasets and distributed systems
Strong understanding of data modeling, data warehousing, and ETL patterns
Experience with CI/CD pipelines and Infrastructure as Code is a plus
Nice to Have
AWS Certification (Data Analytics / Solutions Architect)
Experience with data governance and metadata management
Exposure to data lake and lakehouse architectures
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