AWS Data Quality Engineer: Data Validation & Pipelines

HCLTech; Seattle (WA); On-site; USD 69,000 - 128,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Pharmacy Benefits

Life Insurance

AD&D Insurance

Disability Insurance

Employee Assistance Program

401(k) Retirement Plan

Paid Time Off

Paid Holidays

Job summary

HCLTech is seeking an AWS Data Quality Engineer to join as a full-time role focused on ensuring data quality across AWS data services. The role requires US citizenship or GC holders only, with strong Python and SQL skills and experience in Airflow and Deequ.

The candidate will develop automated data validation scripts, build testing utilities, and implement data quality dashboards, working with data engineers and BI teams to enforce KPIs and SLAs.

Qualifications

• AWS data and storage services (S3, Glue, Redshift, Lambda).
• Python proficiency for automation and tests.
• Airflow and data quality monitoring (Deequ).
• Strong SQL and UNIX/Linux scripting for large datasets.

Responsibilities

• Develop automated scripts to validate data movements and ensure schema, completeness, and timeliness.
• Build robust testing utilities in Python and native AWS services for DataOps workflows.
• Set up data drift monitoring, schema validation, and quality dashboards to preempt failures.
• Translate business rules into data quality KPIs and SLAs with data engineers and BI teams.

Education

AWS Certified Data Engineer – Associate

Tools

AWS Glue

Amazon Redshift

AWS Lambda

Amazon S3

Python

Apache Airflow

Amazon Deequ

SQL

UNIX/Linux scripting

Job description

HCLTech is seeking an AWS Data Quality Engineer to join as a full-time role focused on ensuring data quality across AWS data services. The role requires US citizenship or GC holders only, with strong Python and SQL skills and experience in Airflow and Deequ.

The candidate will develop automated data validation scripts, build testing utilities, and implement data quality dashboards, working with data engineers and BI teams to enforce KPIs and SLAs.