AWS Data Engineer – Qualtrics Integration
Role Type: Contract
Location: Torrance, CA / Remote
Domain: Enterprise Survey Platform / Qualtrics
Primary Stack: AWS | Python | SQL | Qualtrics API
The
AWS Data Engineer – Qualtrics Integration is responsible for designing, building, and maintaining scalable, automated data pipelines that support Qualtrics survey ingestion, transformation, and downstream reporting.
This role focuses on
serverless AWS data engineering, integrating
Qualtrics APIs with AWS services to process structured survey data, dealer hierarchies, and reporting files. The engineer will ensure
data accuracy, automation, monitoring, and performance across end-to-end workflows.
Key Responsibilities
AWS Cloud & Data Engineering
- Design and maintain ETL pipelines using AWS Glue (PySpark)
- Develop AWS Lambda functions (Python / Node.js) for serverless data processing
- Manage AWS S3 for Qualtrics input/output storage and optimized data access
- Orchestrate workflows using AWS Step Functions and MWAA (Airflow)
- Query structured datasets using AWS Athena
- Monitor pipelines using CloudWatch logs and metrics
Data Processing & ETL
- Transform and aggregate Qualtrics datasets (CSV, JSON, XML)
- Merge multiple source files (e.g., dealer master + employee files) into unified hierarchies
- Automate ingestion, transformation, validation, and report generation
- Implement reusable, scalable ETL frameworks
API & Integrations
- Integrate with Qualtrics APIs to extract raw survey data and response files
- Implement event-driven processing (S3 triggers → Lambda)
- Connect AWS pipelines with CRM, ERP, BI tools, or downstream platforms
Data Quality & Reporting
- Perform data validation and quality checks prior to Qualtrics ingestion
- Generate formatted output files aligned to business-defined templates
- Support ad-hoc analysis using SQL and Athena
Programming & Automation
- Develop robust Python scripts for Glue, Lambda, and automation tasks
- Write optimized SQL queries for structured data access
- Use Bash/Shell scripting for file movement and preparation
DevOps & Security (Nice to Have)
- Configure IAM roles and permissions securely
- Implement Infrastructure as Code (Terraform / CloudFormation)
- Support CI/CD pipelines for data workflows
Required Skills & Experience
- Strong experience in AWS Data Engineering & Serverless Architecture
- Hands-on expertise with AWS Glue, Lambda, S3, Athena, Step Functions
- Experience with MWAA (Airflow) for orchestration
- Strong Python and SQL skills
- Experience integrating Qualtrics API or structured survey data
- Ability to troubleshoot pipeline failures and performance issues
Preferred / Nice-to-Have Skills
- Experience with Terraform or CloudFormation
- CI/CD for data pipelines
- Experience supporting BI tools (Power BI, Tableau, etc.)
- Knowledge of data governance and security best practices
Ideal Candidate Profile
- AWS-focused Data Engineer with strong automation mindset
- Comfortable working with survey / VoC / structured data
- Strong debugging, monitoring, and optimization skills
- Able to work independently in enterprise environments
Recruiter Submission Template –
Full Name:
Degree Major with University & Completion Year:
Total Years of Data Engineering Experience:
Total Years of AWS Data Engineering Experience:
Total Years of Experience with Serverless Architecture:
Experience with AWS Glue (PySpark ETL)? (Yes/No – please elaborate):
Experience with AWS Lambda (Python/Node.js)? (Yes/No – please elaborate):
Experience with AWS S3 data storage & optimization?
Experience with AWS Athena? (Yes/No):
Experience implementing data validation & quality checks?
Experience working with Qualtrics API? (Yes/No – please specify):
Experience with event-driven processing (S3 → Lambda)?
Experience integrating AWS with CRM / ERP / BI tools?
Python Experience (years & libraries used):
SQL Experience (years & databases/tools):
Experience with IAM & role-based access?
Brief description of a recent AWS data pipeline you built (tools + outcome):
Motivation / Reason for Interest in Qualtrics Data Engineering Role:
Contact Number:
Email ID:
LinkedIn Profile URL:
Full Address (Street, City, State, Zip):
Notice Period (in weeks):
Current Work Authorization Status:
Expected Hourly Rate:
W2 / C2C (If C2C, corporation name):
Are you comfortable working primarily Remote with Limited Onsite Flexibility?
