We are seeking an experienced AWS Data Engineer / Big Data Technology Lead to design, develop, and maintain scalable data solutions using AWS cloud technologies and big data frameworks. The ideal candidate will have strong experience building data pipelines, managing data platforms, and delivering enterprise-level analytics solutions.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines and architectures on AWS to support analytics, reporting, and operational workflows.
- Develop ETL/ELT solutions using AWS services.
- Build and manage cloud-based data lakes and data warehouse solutions using: Amazon S3, Amazon Athena, Amazon Redshift and Amazon RDS.
- Design and implement scalable data architectures while ensuring data quality, security, and integrity.
- Develop data processing workflows to support large-scale data ingestion and transformation.
- Collaborate with data scientists, analysts, and business teams to curate and optimize production-ready datasets.
- Monitor, troubleshoot, and improve data pipeline performance and reliability.
- Work with DevOps tools and practices including Jenkins and Maven for deployment automation.
Required Experience & Skills
- 7+ years of experience in Big Data Engineering, Data Engineering, or related roles.
- Strong hands-on experience with AWS cloud services and Big Data technologies.
- Experience designing, developing, and maintaining scalable data pipelines and cloud-based data architectures.
- Proficiency in developing ETL/ELT pipelines using AWS services such as AWS Glue, Lambda, Kinesis, and Step Functions.
- Experience building and managing data lakes and data warehouses using AWS services including S3, Athena, Redshift, and RDS.
- Strong knowledge of data modeling, data integration, and ensuring data quality and integrity.
- Experience working with Hadoop and other Big Data technologies.
- Strong SQL skills with experience in databases such as Oracle 10g/11g/12c and SQL Server.
- Experience with Unix/Linux environments and scripting.
- Familiarity with DevOps tools such as Jenkins and Maven.
- Experience with monitoring and logging tools such as Splunk is preferred.
- Ability to collaborate with data scientists, analysts, and cross-functional teams to deliver production-ready data solutions.