Data Engineer - AWS

Responsibilities

• Build/support applications using speech-to-text AWS services like Transcribe, Comprehend, along with Bedrock.
• Experience working with BI tools like QuickSight.
• Design, build, and manage scalable data pipelines using AWS services (e. g., Glue, Lambda, Step Functions, S3 EMR, Kinesis, Snowflake).
• Optimize data storage and retrieval for large-scale datasets in data lakes or data warehouses.
• Monitor, debug, and optimize the performance of data jobs and workflows.
• Ensure data quality, consistency, and security across environments.
• Collaborate with analytics, engineering, and business teams to understand data needs.
• Automate infrastructure deployment using IaC tools like CloudFormation or Terraform.
• Apply best practices for cloud cost optimization, data governance, and DevOps.
• Stay current with AWS services and recommend improvements to data architecture.
• Understanding machine learning pipelines and MLOps (nice to have).
  

Requirements

• Bachelor's degree in computer science or a related field.
• 5+ years of experience as a Data Engineer, with at least 3 years focused on AWS.
• Strong experience with AWS services, including Transcribe, Bedrock, and QuickSight.
• Familiarity with Glue, S3 Snowflake, Lambda, Step Function, Kinesis, Athena, EC2/EMR, Power BI, or Tableau.
• Proficient in Python, PySpark, or Scala for data engineering tasks.
• Hands-on experience with SQL and data modeling.
• Familiarity with CI/CD pipelines and version control (e. g., Git, CodePipeline).
• Experience with orchestration tools (e. g., Airflow, Step Functions).
• Knowledge of data security, privacy, and compliance standards (GDPR, HIPAA, etc. ).
  

Good To Have Skills

• AWS certifications (e. g., AWS Certified Data Analytics - Specialty, AWS Certified Solutions Architect).
• Experience with containerization (Docker, ECS, EKS).
• Experience working in Agile/Scrum environments.