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# Senior Technical Lead

- **Company:** HCL America Inc.
- **Location:** King, NC, United States
- **Experience:** Expert
- **Salary:** $148,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Data Transformation, Python (Programming Language), DataOps, SQL Databases, Workflow Management Systems, AWS Lambda, AWS Glue, Amazon Redshift
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1f7d62df63492eaf

## About the Role

Deep proficiency in AWS data and storage services like Amazon S3, AWS Glue, Amazon Redshift, and AWS Lambda .
Advanced proficiency in Python for building automation frameworks and orchestrating data tests .
Experience with orchestration tools (e.g., Apache Airflow ), data transformation, and data quality monitoring frameworks (e.g., Amazon Deequ ).
Strong command of SQL and UNIX/Linux scripting for querying and analyzing large-scale, distributed datasets

Certifications: Candidate with AWS Certified Data Engineer - Associate - Highly preferred

## Description

Develop automated scripts to validate source-to-target data movements, ensuring schema consistency, completeness, and timeliness.
Build robust, reusable testing utilities using Python , native AWS services that integrate seamlessly into DataOps workflows.
Set up continuous data drift monitoring, schema validation, and data quality dashboards to proactively catch pipeline failures.
Partner with data engineers, BI teams, and software engineers to translate business rules into strict data quality KPIs and SLAs.
Key Responsibilities
Develop automated scripts to validate source-to-target data movements, ensuring schema consistency, completeness, and timeliness.
Build robust, reusable testing utilities using Python , native AWS services that integrate seamlessly into DataOps workflows.
Set up continuous data drift monitoring, schema validation, and data quality dashboards to proactively catch pipeline failures.
Partner with data engineers, BI teams, and software engineers to translate business rules into strict data quality KPIs and SLAs.


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